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Journal articles on the topic 'Process Automation and Optimization'
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Janik, Dawid. "Optimization of License Management for Business Process Automation with Robotic Process Automation." International Journal of Contemporary Management 60, no. 1 (2024): 280–89. https://doi.org/10.2478/ijcm-2024-0017.
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Abstract Purpose Nowadays automation of business processes is increasing in popularity among organizations. The undeniable benefits, like freeing up the potential and time of well-educated employees so they can focus on more valued activities, are well known. With the growing number of automated processes, optimization of bot usage has become a significant factor in reducing costs. This paper presents an analysis supporting the proper management of business process automation in the context of bots’ license usage. Design The goal of this research is to build a tool that allows for the analysis of license demand and supports automated management of process runs. Findings For the described case, the research estimated the minimum number of bot licenses to ensure that the automated processes would run in a stable manner, and identified the time periods of different license utilization levels. Practical implication The described analysis is a tool for cost optimization of current bot infrastructure. It supports the continuous improvement processes for automated processes run management with balanced levels of license utilization. Value The analysis can be easily adopted for different setups of bot infrastructure to bring about benefits of cost optimization.
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Sachkirat Singh Pardesi. "Integrating Hyper-Automation with RPA and AI for End-to-End Business Process Optimization." Darpan International Research Analysis 12, no. 3 (2024): 199–211. http://dx.doi.org/10.36676/dira.v12.i3.67.
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The combination of Hyper-Automation, Robotic Process Automation (RPA), and Artificial Intelligence (AI) has become a game-changing method for improving business processes from start to finish in the quickly changing field of business technology. The important definitions, foundations, evolution, significance, research gaps, and the need for this study in the contemporary corporate climate will all be covered in detail in this extensive introduction. The use of cutting-edge technology, such as RPA and AI, to automate processes in a way that goes beyond the scope of conventional automation is known as hyper-automation. Hyper-automation is the process of automating any repetitious work that may be done so that businesses can become more efficient, cut expenses, and simplify their operations. The term "robotic process automation," or RPA, describes the use of software "bots" to automate regular and highly repetitive processes that are normally completed by human personnel. Artificial intelligence, or AI, is the study of how computers, especially computer systems, can mimic human cognitive functions including learning, reasoning, and self-correction.
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Mahaboobsubani, Shaik. "Robotic Process Automation for Telecom Billing Optimization." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 2 (2019): 1–9. https://doi.org/10.5281/zenodo.14352186.
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Robotic Process Automation is the new face of the telecom billing system, as it streamlines processes, reduces errors, and increases efficiency. This research investigates early adoption of RPA for the design, implementation, and operational metrics implications of telecom billing. Key findings include significant improvements in accuracy, substantial gains in terms of time saved due to automated workflows taking over repetitive and error-prone tasks performed by humans. Automation would serve to illustrate the potential transformative impact when stacked against traditional billing methods. Integration of systems involves certain complexities and thus breakdown or failure points, which needs to be compensated for through strong design and monitoring frameworks. The present study measures error rates, process automation workflows, and efficiency metrics that have provided great insights into optimizing the telecom billing system with RPA. This emphasizes strategic planning and flexibility to adequately realize the benefits of RPA while mitigating risks associated with dynamic telecom environments
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Silva, Roberto De Carvalho. "AUTOMATION AND OPTIMIZATION OF THE SAP SOFTWARE INSTALLATION PROCESS IN CORPORATE WORKSTATIONS: IMPACTS ON SCALABILITY AND SECURITY." Revista ft 29, no. 145 (2025): 34–35. https://doi.org/10.69849/revistaft/ch10202504040734.
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The automation and optimization of SAP software installation in corporate workstations are critical for enhancing scalability, security, and efficiency. Manual deployment processes are time-consuming, error-prone, and challenging to standardize in large-scale environments. This study explores automation tools such as Ansible, Terraform, and SCCM to streamline SAP deployment, reducing manual intervention and ensuring consistent configurations across multiple devices. The research also highlights the significance of security measures, including system hardening, least privilege policies, and multi-factor authentication, to mitigate potential vulnerabilities. A comprehensive literature review examines recent advancements in SAP deployment automation, emphasizing scalability and security. Studies demonstrate that integrating automation with cloud-based infrastructures improves deployment consistency, while compliance automation frameworks facilitate regulatory adherence. Additionally, AI-driven monitoring solutions enhance system reliability by predicting failures and optimizing resource allocation. The discussion highlights how automation tools significantly reduce deployment time while improving traceability and security. Standardized installation protocols prevent misconfigurations, and centralized deployment management minimizes human errors. Moreover, implementing security best practices safeguards SAP environments against cyber threats, ensuring compliance with regulatory requirements. This study concludes that automating SAP installation is a strategic necessity for organizations seeking operational efficiency, security, and scalability. Future research should explore AI-based automation to further refine installation processes and enhance proactive threat detection. By adopting automation and security best practices, organizations can achieve a more resilient and efficient SAP deployment framework.
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Nisbett, Alex, and Gareth Johnston. "Wireless Technology for Process Automation." Measurement and Control 44, no. 5 (2011): 150–53. http://dx.doi.org/10.1177/002029401104400503.
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Samaranayake, Premaratne. "Business process integration, automation, and optimization in ERP." Business Process Management Journal 15, no. 4 (2009): 504–26. http://dx.doi.org/10.1108/14637150910975516.
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Chinthamu, Narender, Ashish, Victor Emmanuel P, Mathiyalagan P, Syed Zahidur Rashid, and Jothi E. "Robotic Process Automation in Business Processes Streamlining Operations Through Automation Technologies." ITM Web of Conferences 76 (2025): 01005. https://doi.org/10.1051/itmconf/20257601005.
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RPA also known as Robotic Process Automation is revolutionizing the way businesses operate by automating mundane tasks, increasing operational efficiency, and lowering the overall operational cost. Yet, researchers study theoretical benefits prevalent in the State of the Art, without validating them empirically, or studying their impact across industries or in the long term. About the study This study aspires to fill these gaps, offering a data and challenge-driven and scalable framework of RPA adoption. In this study, we incorporate real-world perspectives, AI-supported automation, and process mining methods in the quest for RPA optimization, differing from earlier investigations. RPA long-term return on investment, workforce collaboration, ethical auditing, security challenges and responsibility in automation. We also present a scalable and adaptive RPA framework for SMEs as well as large enterprises. In addition, our research findings prove that AI-enabled RPA increases operational efficiency and helps firms enhance decision-making with the aid of machine learning (ML) and natural language processing (NLP). This study proposes a detailed roadmap for sustainable and intelligent automation in business processes by considering scalability, security, compliance, and workforce integration.
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Gui, Xue, Xiao Yan Zheng, Jian Wei Song, and Xia Peng. "Automation Bridge Design and Structural Optimization." Applied Mechanics and Materials 63-64 (June 2011): 457–60. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.457.
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This paper summarizes the structural optimization applications in civil engineering design and development of the situation based on the characteristics of the bridge structure design process is proposed for the bridge project to the genetic algorithm, neural network, expert system technology as the basis for combining automated design and optimization of structural design of the system; as basic idea, given the structure design of automation system design and optimization of the overall design framework, and prestressed beam design automation is simply an example of structural design and optimization of design automation. Finally, a brief summary of the development process of bridge design software, design automation and optimization that the inevitable trend of development.
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Kazenwadel, Benjamin, Simon Becker, Marina Graf, and Marcus Geimer. "Aligning process quality and efficiency in agricultural soil tillage." at - Automatisierungstechnik 71, no. 11 (2023): 979–86. http://dx.doi.org/10.1515/auto-2023-0042.
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Abstract Automation in agricultural machinery is a crucial driver of productivity and sustainability. Some automation features like automated steering and real-time data analytics are already state-of-the-art. On the other hand, a human driver performs the optimization of the working speed manually, and the automation of this is an ongoing challenge. Process quality and process efficiency are the two main targets in this optimization. Agricultural soil tillage requires achieving both. Therefore, the correlation between process quality optimization and process efficiency is fundamental, and vice versa. The approach presented in this paper shows how the two optimization targets of efficiency and process quality can be optimized and aligned together. Optical sensors determine various parameters to describe and model the process quality. The measured machine state determines the characteristics of the interaction forces between the machine and the environment. A machine learning algorithm describes the relationships in the drivetrain. The two process targets are each predicted for different working speeds and are combined in the form of a boundary target and an optimization target to identify one optimized target speed value.
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Legito. "Examining the Effects of Robotic Process Automation on Operational Efficiency and Business Process Optimization (Literature Study)." West Science Interdisciplinary Studies 1, no. 02 (2023): 84–93. https://doi.org/10.58812/wsis.v1i2.91.
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This research study investigates the influence of Robotic Process Automation (RPA) on operational efficiency and business process optimization. The study adopted a systematic literature review approach to collect and analyze relevant academic articles, industry reports, and conference proceedings. Findings reveal that RPA implementation significantly improves operational efficiency by automating repetitive and rule-based tasks. This automation reduces manual effort, minimizes errors, and speeds up process execution, leading to increased productivity, faster response times, and cost savings for organizations. RPA also contributes to business process optimization by streamlining workflows, eliminating bottlenecks, and standardizing processes. Standardized execution of tasks through RPA improves process efficiency, quality, and visibility, allowing organizations to make data-driven decisions and continuously optimize their operations. However, challenges such as process complexity, employee resistance to change, integration issues, and data security concerns need to be addressed for a successful RPA implementation. Organizations must develop a comprehensive implementation plan, establish a governance framework, and foster a culture of collaboration to overcome these challenges. The practical implications derived from this research offer guidance for organizations that are considering or implementing RPA solutions. Recommendations include conducting a thorough process analysis, developing a comprehensive implementation plan, establishing governance mechanisms, encouraging collaboration, and continuously optimizing processes. While this study provides valuable insights into the impact of RPA on operational efficiency and business process optimization, it acknowledges the limitations of the research, such as the reliance on literature up to September 2021 and the subjectivity of the data analysis.
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Rama Krishna Debbadi and Obed Boateng. "Enhancing cognitive automation capabilities with reinforcement learning techniques in robotic process automation using UiPath and automation anywhere." International Journal of Science and Research Archive 14, no. 2 (2025): 733–52. https://doi.org/10.30574/ijsra.2025.14.2.0450.
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Cognitive automation represents the next frontier in Robotic Process Automation (RPA), enabling systems to learn, adapt, and optimize decision-making processes dynamically. Traditional RPA platforms, such as UiPath and Automation Anywhere, excel in automating rule-based tasks but lack the ability to handle complex, evolving scenarios that require adaptive intelligence. Integrating reinforcement learning (RL) techniques into RPA workflows offers a transformative approach to enhancing cognitive automation capabilities. RL enables bots to make intelligent, data-driven decisions by learning from their environment, optimizing workflows, and improving operational efficiency over time. This study explores the integration of RL algorithms within UiPath and Automation Anywhere to develop self-learning automation systems capable of handling non-deterministic processes. Key applications include intelligent exception handling, dynamic process optimization, and adaptive customer service automation. By leveraging RL-based decision models, RPA bots can continuously improve their performance, reduce error rates, and optimize workflows beyond predefined rules. The research also examines challenges such as computational complexity, model interpretability, and integration barriers within enterprise automation environments. Solutions such as cloud-based reinforcement learning frameworks, hybrid AI-RPA architectures, and explainable AI techniques are proposed to mitigate these challenges. The findings indicate that reinforcement learning can significantly enhance cognitive automation in RPA, enabling businesses to achieve higher levels of efficiency, adaptability, and intelligent decision-making.
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John, Selvaraj Arulappan. "AI in Payroll: Unlocking Efficiency through Process Discovery and Automation Workflows." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 1 (2025): 1615–18. https://doi.org/10.5281/zenodo.14792213.
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The application of Artificial Intelligence (AI) in payroll systems is revolutionizing traditional workflows by enabling process discovery and automation. AI-driven tools analyze operational data to identify inefficiencies, uncover hidden patterns, and streamline payroll processes. Through intelligent automation, tasks such as payroll calculations, tax compliance, and error detection are executed with greater speed and accuracy, reducing manual interventions and associated costs. Advanced algorithms facilitate real-time monitoring and optimization, ensuring compliance with evolving regulations while enhancing system resilience against fraud and anomalies. This paper delves into the transformative role of AI in automating payroll workflows [1], showcasing how process discovery methodologies uncover bottlenecks and drive operational efficiency. Case studies are presented to highlight the measurable impacts of AI-powered automation on cost reduction, workforce productivity, and strategic decision-making. As organizations adapt to an AI-driven payroll landscape, the shift toward automated workflows signifies a new era of financial and operational excellence.
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Timus, Mihai, Liviu Ciucan-Rusu, Daniel Stefan, and Maria-Alexandra Popa. "Student Relationship Management Optimization Using Organizational Process Automation Tools." Acta Marisiensis. Seria Oeconomica 14, no. 1 (2020): 31–40. http://dx.doi.org/10.2478/amso-2020-0004.
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Abstract Organizational process optimizations are crucial to meet nowadays challenges, especially in the educational institution environment, where information flow is very intense, and actors involved are different from decision making perspective. The relations with students are one of the most important aspect of educational institutions processes, therefore, the more automated and digitalized is this process, the more attention can be invested to continuous improvement of other organizational processes. Our study intends to promote continuous improvement of student relationship management of universities by active usage of ICT solutions available in organization and prepare internal regulations and staff for this transformation.
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Kim, Dong-Hoon, and Jun-Yeob Song. "Key Technology Analysis for Machining Process Optimization and Automation." Journal of The Korean Society of Manufacturing Technology Engineers 22, no. 2 (2013): 179–84. http://dx.doi.org/10.7735/ksmte.2013.22.2.179.
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Kudryavtsev, Evgeniy. "Automation of optimization of discrete technological processes." MATEC Web of Conferences 196 (2018): 04067. http://dx.doi.org/10.1051/matecconf/201819604067.
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The article describes the procedure automation of optimization discrete technological processes with using of Bellman’s functional (recurrent) equation and system Mathcad. As rule the technological processes includes n of operations and each operation can be executed by various types of equipment. Expenses (cost, time, …) on execution of i operation by k equipment after execution by j equipment (i-1) operation are known - c (i, j, k). Expenses for execution by k equipment i operation can depend on the equipment - j, which executed previous (i-1) operation. It is necessary to execute automation of optimization technological process with the minimum expenses. The algorithm of the decision of a problem by Bellman’s method includes two phases. The first phase is calculations of the minimum expenses for execution of all partial technological processes, from last operation of process to the first. The second phase is definition of the required optimum set of equipment which is carrying out all technological process with the minimum expenses. The proposed procedure of automation of optimization technological process using Bellman’s method and system Mathcad significantly decreases time and labour costs on execution of such calculations and efficiently to execute investigations related with change of equipment parameters.
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Madireddy, Ravindra Reddy. "Evolutionary Trends in Agentic Automation: From Simple Bots to Intelligent Agents." European Journal of Computer Science and Information Technology 13, no. 17 (2025): 99–110. https://doi.org/10.37745/ejcsit.2013/vol13n1799110.
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The evolution of automation technology has progressed through three distinct waves, transforming from simple rule-based systems to sophisticated agentic automation. This article traces this evolutionary journey, examining how Robotic Process Automation (RPA) established foundations for efficiency while the integration of artificial intelligence capabilities expanded automation's scope and resilience. The emergence of Agentic Process Automation (APA) represents the frontier of this evolution, enabling autonomous learning, contextual decision-making, and self-directed optimization. The technical foundations of APA systems are explored, including reinforcement learning frameworks, multi-agent architectures, and explainable AI components that enable increasingly sophisticated capabilities. The article addresses implementation challenges such as knowledge representation, safety controls, and legacy system integration, highlighting effective technical solutions. Finally, future investigation directions and industry applications are examined, including cross-domain generalization, ethical decision frameworks, and transformative applications across financial services, healthcare, and manufacturing sectors.
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Fanoos, Madonna, Abeer Hamdy, and Khaled A. Nagaty. "Bug Triage Automation Approaches." International Journal of Open Source Software and Processes 13, no. 1 (2022): 1–19. http://dx.doi.org/10.4018/ijossp.313183.
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Bug triage is an essential task in the software maintenance phase. It is the process of assigning a developer (fixer) to a bug report. A personnel (triager) has to analyze the developers' profiles and bug reports for the purpose of making a suitable assignment. Manual bug triage consumes time and effort, so automating this process is a necessity. The previous research studies addressed the triage problem as an information retrieval problem, where the new bug report is the query. Other researchers tackled this problem as a classification problem and utilized traditional machine learning or deep learning techniques. A handful of research studies handled this problem as an optimization problem and utilized optimization algorithms such as Hungarian. This paper briefs and analyzes the previous bug triage approaches in addition to conducting an empirical comparison among five of the previous approaches.
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Abdrakhmanova, N. B., M. K. Bagramova, A. V. Dudina, and K. A. Sizikova. "AUTOMATION OF ACCOUNTING." Vestnik of M. Kozybayev North Kazakhstan University, no. 4 (60) (January 30, 2024): 92–97. http://dx.doi.org/10.54596/2958-0048-2023-4-92-97.
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This article discusses current trends and problems of automation implementation in accounting of organizations. The main stages of the automation process are described, starting from the analysis and selection of suitable software to staff training and process optimization. The article emphasizes the importance of automation to improve the efficiency, accuracy and efficiency of accounting. Common programs and technologies are considered, as well as the advantages and challenges faced by enterprises when implementing automation in accounting processes are highlighted. The article emphasizes the importance of a balanced use of technology and the human factor for the successful fusion of automation with accounting practice.
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ХЫДЫРОВ, А. Х. "OPTIMIZATION OF EMPLOYEE ONBOARDING PROCESS USING AN AUTOMATED SYSTEM." Экономика и предпринимательство, no. 7(168) (August 6, 2024): 1124–27. http://dx.doi.org/10.34925/eip.2024.168.7.224.
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Автоматизация процессов адаптации персонала представляет собой актуальную область исследований в современном управлении человеческими ресурсами. В данной статье рассматривается важность и эффективность внедрения автоматизированных систем для обеспечения успешной адаптации новых сотрудников в организации. Описывается принципиальная схема автоматизированной системы адаптации, включающая этапы от назначения задач до мониторинга и контроля выполнения. Преимущества автоматизации включают сокращение временных и финансовых затрат, повышение качества процесса и улучшение восприятия компании сотрудниками. Дополнительные возможности автоматизации, такие как мониторинг выполнения задач и интеграция с системами управления персоналом, также рассматриваются. Результаты представленной работы могут быть полезны как для исследователей в области управления персоналом, так и для практиков, заинтересованных в оптимизации процессов адаптации в своих организациях. Automation of personnel adaptation processes represents a vital area of research in contemporary human resource management. This article explores the importance and effectiveness of implementing automated systems to ensure successful adaptation of new employees within organizations. It describes the fundamental framework of an automated adaptation system, encompassing stages from task assignment to monitoring and control of execution. Advantages of automation include reduction in time and financial costs, enhancement of process quality, and improvement in employee perception of the company. Additional automation capabilities, such as task monitoring and integration with personnel management systems, are also discussed. The findings of this work can be valuable for both researchers in the field of personnel management and practitioners interested in optimizing adaptation processes in their organizations.
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Venigandla, Kamala, Navya Vemuri, Naresh Thaneeru, and Venkata Manoj Tatikonda. "Leveraging AI-Enhanced Robotic Process Automation for Retail Pricing Optimization: A Comprehensive Analysis." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 361–70. http://dx.doi.org/10.60087/jklst.vol2.n2.p370.
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Pricing strategies are of paramount importance in the fiercely competitive retail sector, exerting a substantial influence on a company's financial performance and market standing. The amalgamation of artificial intelligence (AI) and robotic process automation (RPA) presents merchants with a potentially revolutionary opportunity to include and augment their pricing strategies via automation. The present research article investigates the field of AI-enhanced Robotic Process Automation (RPA) within the realm of retail pricing. It aims to analyses the impact of RPA on decision-making processes, operational efficiency, and overall organizational success. This research offers a thorough examination of pertinent scholarly works, empirical examples, and theoretical frameworks to investigate the advantages, challenges, and potential future trajectories associated with the utilization of artificial intelligence (AI) and robotic process automation (RPA) to augment pricing strategies in the retail sector.
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Sri, Rama Chandra Charan Teja Tadi. "Process Mining Driven by Deep Learning for Anomaly Detection in Intelligent Automation Systems." Journal of Scientific and Engineering Research 11, no. 1 (2024): 317–29. https://doi.org/10.5281/zenodo.15100742.
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Intelligent automation revolutionizes enterprise operations, software orchestration, and financial systems by integrating AI-driven decision-making, real-time workflow optimization, and large-scale automated execution. However, ensuring system security, operational efficiency, and adaptability in such dynamic environments poses significant challenges. Additionally, heterogeneous automation ecosystems, incorporating cloud-based microservices, robotic process automation (RPA), and distributed AI agents, demand a scalable and adaptive anomaly detection paradigm that can effectively operate across multi-domain environments, particularly in the banking and financial sector, where real-time fraud detection and compliance monitoring are critical. This theoretical concept envisions a deep learning-driven process mining methodology continuously evolving alongside automation workflows, offering a proactive approach to anomaly detection in .NET-based enterprise applications. This paradigm employs multi-layered workflow analysis, anomaly inference through graph neural networks (GNNs), deep feature extraction, and reinforcement learning-driven optimization to deliver a scalable, self-adaptive anomaly detection mechanism. Additionally, the approach integrates semantic workflow analysis, automated event correlation modeling, and multi-objective optimization to refine anomaly classification granularity and predictive modeling accuracy. By addressing high-dimensional event interdependencies, context-aware deviation analysis, and anomaly reasoning, this model aims to establish a resilient and transparent automation security paradigm that enables real-time workflow intelligence, cross-domain adaptability, and self-improving anomaly mitigation strategies for financial risk assessment, automated loan processing, and fraud analytics in banking environments. Future extensions of this theoretical approach will explore Interpretable Machine Learning (IML), adversarial robustness in deep anomaly detection, and blockchain-based anomaly verification to enhance anomaly interpretability, security, and compliance in enterprise automation ecosystems.
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Tian, Weihua, Xiangbin Meng, Jianing Wang, and Hongkui Yan. "Optimization of Distribution Automation System Based on Artificial Intelligence Wireless Network Technology." Journal of Sensors 2022 (September 17, 2022): 1–8. http://dx.doi.org/10.1155/2022/1646667.
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In order to further improve the automation setting of distribution system, this paper proposes an optimization research of distribution automation system based on artificial intelligence wireless network technology. This method uses artificial intelligence wireless network technology to optimize the automation of distribution system and improve the automation ability of distribution system. The experimental results show that when the number of iterations in the training process reaches 338, the mean square error is 0.001. Conclusion. The optimization research method of distribution automation system based on artificial intelligence wireless network technology can more effectively improve the automation level of distribution system.
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Wang, She Wei, Rong Mo, and Hai Cheng Yang. "Quantitative and Meticulous Methods to Aero-Engine Assembly Process." Applied Mechanics and Materials 220-223 (November 2012): 206–9. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.206.
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Manual operation and paper document based management are the bottleneck to the quality and efficiency of aero-engine assembly process. A set of optimization methods is presented about the aero-engine assembly process. From the technical point, the model of LoA (Level of Automation) was expanded with the addition of the role dimension and task dimension to construct a quantified method of level of assembly information automation, which was utilized to guide the digitalization of assembly process. From the management point, the meticulous management was utilized to improve the microcosmic operation and macro flow control of assembly. A refine and quantified assembly digital system for aero-engine has been developed to verify the effectiveness of the optimization methods.
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Gunawan, Ali. "Robotic Processes Automation to Improve Business Process Automation: A Systematic Literature Reviews." E3S Web of Conferences 426 (2023): 01009. http://dx.doi.org/10.1051/e3sconf/202342601009.
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Technological developments have been very fast since 2000 until now, and there are many information system applications in all corners of the world. Now, many services to customers or the public are provided by companies, from small to enterprise software, and many institutions already use the platform— digital services to serve customers and society. Industry 4.0 is the fourth industrial revolution since 2010, where technology and accelerated processes have used automation and are accelerating. This technology is Robotic Process Automation (R.P.A) which could automate many organizational business processes and use Artificial Intelligence (AI) algorithms and techniques. Complementarity makes it possible to increase the accuracy, efficiency, effectiveness, and implementation of automation processes in business processes, in recognition, classification, forecasting, and process optimization. This study aims to provide research information on the application of R.P.A related to AI, which can contribute to improving organizational processes related to Industry 4.0. Researchers found that automation with R.P.A has been widely applied in various industries, various implementations of R.P.A Technology have been carried out, and many publishers have published on this topic.
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de Silva, C. W., and F. Omar. "Automation and Optimization of the Can-Filling Process of Fish." IFAC Proceedings Volumes 31, no. 20 (1998): 337–42. http://dx.doi.org/10.1016/s1474-6670(17)41817-9.
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Adrita, Mumtahina Mahajabin, Alexander Brem, Dominic O’Sullivan, Eoin Allen, and Ken Bruton. "Methodology for Data-Informed Process Improvement to Enable Automated Manufacturing in Current Manual Processes." Applied Sciences 11, no. 9 (2021): 3889. http://dx.doi.org/10.3390/app11093889.
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Manufacturing industries are constantly identifying ways to automate machinery and processes to reduce waste and increase profits. Machines that were previously handled manually in non-standardized manners can now be automated. Converting non-digital records to digital formats is called digitization. Data that are analyzed or entered manually are subject to human error. Digitization can remove human error, when dealing with data, via automatic extraction and data conversion. This paper presents methodology to identify automation opportunities and eliminate manual processes via digitized data analyses. The method uses a hybrid combination of Lean Six Sigma (LSS), CRISP-DM framework, and “pre-automation” sequence, which address the gaps in each individual methodology and enable the identification and analysis of processes for optimization, in terms of automation. The results from the use case validates the novel methodology, reducing the implant manufacturing process cycle time by 3.76%, with a 4.48% increase in product output per day, as a result of identification and removal of manual steps based on capability studies. This work can guide manufacturing industries in automating manual production processes using data digitization.
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Ramesh Pingili. "AI-driven intelligent document processing for banking and finance." International Journal of Management & Entrepreneurship Research 7, no. 2 (2025): 98–109. https://doi.org/10.51594/ijmer.v7i2.1802.
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The banking and finance industry is buried in paperwork—loan applications, compliance reports, risk assessments, and fraud investigations. Manual processing and outdated automation slow operations, increase costs and expose institutions to compliance risks (Vaultedge, 2023). AI-driven Intelligent Document Processing (IDP) is changing this by automating document workflows, accelerating approvals, and enhancing fraud detection. AI-powered IDP integrates machine learning, NLP, and RPA to reduce verification times, reduce errors, and strengthen compliance monitoring. Banks using AI-driven document automation process loan approvals 70% faster, improve fraud detection rates by 50%, and lower compliance costs by 40% (Rajput et al., 2025). This paper explores real-world applications of AI in banking document processing, highlighting efficiency gains, challenges, and future potential. As financial institutions move toward self-learning AI models, IDP is set to become a critical driver of speed, accuracy, and security in banking operations. Keywords: AI-Driven Document Processing, Banking Automation, Fraud Detection, Regulatory Compliance, Machine Learning in Finance, Robotic Process Automation (RPA), Intelligent Workflow Optimization.
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Nambiar, Sanjay, Anan Ashrabi Ananno, Herman Titus, Anton Wiberg, and Mehdi Tarkian. "Multidisciplinary Automation in Design of Turbine Vane Cooling Channels." International Journal of Turbomachinery, Propulsion and Power 9, no. 1 (2024): 7. http://dx.doi.org/10.3390/ijtpp9010007.
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In the quest to enhance the efficiency of gas turbines, there is a growing demand for innovative solutions to optimize high-pressure turbine blade cooling. However, the traditional methods for achieving this optimization are known for their complexity and time-consuming nature. We present an automation framework to streamline the design, meshing, and structural analysis of cooling channels, achieving design automation at both the morphological and topological levels. This framework offers a comprehensive approach for evaluating turbine blade lifetime and enabling multidisciplinary design analyses, emphasizing flexibility in turbine cooling design through high-level CAD templates and knowledge-based engineering. The streamlined automation process, supported by a knowledge base, ensures continuity in both the mesh and structural simulation automations, contributing significantly to advancements in gas turbine technology.
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Rudd, John B. "Process control and automation. Multivariable Process Unit Optimization in Pulp and Paper Applications." JAPAN TAPPI JOURNAL 52, no. 3 (1998): 397–406. http://dx.doi.org/10.2524/jtappij.52.397.
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Adekunle, Bolaji Iyanu, Ezinne C. Chukwuma-Eke, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "Machine Learning for Automation: Developing Data-Driven Solutions for Process Optimization and Accuracy Improvement." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 1 (2021): 800–808. https://doi.org/10.54660/.ijmrge.2021.2.1.800-808.
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Machine learning (ML) has emerged as a transformative technology for automation, enabling data-driven solutions that enhance process optimization and accuracy across various industries. By leveraging vast datasets, ML algorithms identify patterns, automate decision-making, and continuously improve system performance. This explores the role of ML in automation, focusing on its applications in predictive maintenance, quality control, supply chain optimization, and real-time monitoring. Supervised and unsupervised learning techniques, along with reinforcement learning, are instrumental in refining processes, reducing operational costs, and increasing efficiency. Key benefits of ML-driven automation include minimizing human intervention, reducing errors, and enhancing predictive capabilities. Industries such as manufacturing, healthcare, finance, and logistics are increasingly adopting ML solutions to streamline workflows and optimize decision-making. Advanced ML models, including deep learning and neural networks, contribute to enhanced accuracy by processing complex data structures and making real-time adjustments based on historical trends. Challenges in ML automation, such as data quality, model interpretability, and integration with existing systems, require strategic approaches to ensure seamless implementation. Ethical considerations, including algorithmic bias and data privacy, must also be addressed to foster responsible AI adoption. Future advancements in ML, including the integration of edge computing and explainable AI, will further enhance automation capabilities, making processes more adaptive and intelligent. This highlights the impact of ML-driven automation on efficiency and accuracy, emphasizing its role in shaping the future of data-driven decision-making. By understanding the core principles, applications, and challenges, organizations can leverage ML to drive innovation and operational excellence.
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Antonyuk, Vladislav, and Maryna Sydorova. "INTEGRATION AND USE OF ARTIFICIAL INTELLIGENCE FOR AUTOMATED MACROS CREATION." System technologies 5, no. 154 (2024): 16–23. http://dx.doi.org/10.34185/1562-9945-5-154-2024-02.
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In today's world, automation and optimization of work processes are becoming key success factors. This work examines the combination of automation systems and artificial intelligence (AI) and their impact on the optimization of work processes. The technology of integration into the process automation system and learning of a large language model for the automated creation of macros using the example of the author's software "Draw & GO" has been developed and proposed.
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Singh, Chandra Prakash. "The Role of RPA in Transforming DevOps: Driving CI/CD Efficiency and Beyond." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–6. https://doi.org/10.55041/ijsrem8160.
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The COVID-19 pandemic has profoundly reshaped the global business environment, compelling organizations to accelerate their digital transformation journeys. Remote work has become the standard, fueling an unprecedented demand for swift and dependable software delivery. This white paper delves into how Robotic Process Automation (RPA) can revolutionize DevOps practices, particularly through the automation of Continuous Integration and Continuous Deployment (CI/CD) pipelines. By integrating RPA, organizations can eliminate manual bottlenecks, enhance accuracy, and scale their software delivery processes effectively. Keywords Robotic Process Automation (RPA), DevOps Automation, Continuous Integration (CI), Continuous Deployment (CD), CI/CD Pipelines, Digital Transformation, Remote Work Efficiency, Real-Time Testing, Error Reduction, Process Scalability, Compliance Automation, Labor Optimization, Cost Efficiency, Workflow Automation, Incident Management Automation
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Linnala, Mikko, and Jari Hämäläinen. "PAPER PHYSICS. Bi-level optimization in papermaking process design." Nordic Pulp & Paper Research Journal 27, no. 4 (2012): 774–82. http://dx.doi.org/10.3183/npprj-2012-27-04-p774-782.
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Abstract Model-based optimization is a sophisticated design tool for papermaking processes. In this paper, the method is extended from the unit process design and single-level optimization to the simultaneaus design of the process structure and operations. This is enabled by using a bi-level optimization formulation which allows to avoid unnecessary iterations between the process and automation designs. The bi-level optimization approach is studied here from the perspective of multiobj ective optimization and decision making. The method is illustrated by a case study in which the broke and water system structures and the papermaking process operations are optimized simultaneously.
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Scattini, Noel, and Stanislaw Paul MAJ. "Aquaponics – A Process Control Approach." Modern Applied Science 11, no. 11 (2017): 43. http://dx.doi.org/10.5539/mas.v11n11p43.
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An aquaponics automation design was undertaken to interpret the system requirements to integrate automation to operate and optimize the system. The system was designed to increase the layers of control over the inputs and outputs to operate the system with a process control approach. The viability of these levels of control over the process was investigated by undertaking a processes design to assess types of instrumentation required and control functions that could be incorporated into the design to optimize the process. The design process incorporated sub-systems that did not rely on a main system, to increase ranges of commercially viable crops. The subsystems do not have the same environmental requirements of the main system and the subsystems environment could be calibrated to meet specific requirements of a selected crop including fruiting vegetable types. The results of the automation design have been tabulated into this article to assess the viability of increased levels of process control to obtain subsystem designs with maximized optimization.
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Ranadheer Suram. "Integration of Emerging Technologies for Business Workflow Optimization: A Systematic Analysis of IoT, AI, and Blockchain Solutions." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 6 (2024): 1995–2003. https://doi.org/10.32628/cseit2410612390.
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The rapid evolution of emerging technologies presents unprecedented opportunities for optimizing business workflows through integrated automation solutions. This article examines the convergence of the Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technologies in transforming traditional business processes. Through systematic analysis, the article investigates how IoT enables system automation, while machine learning algorithms facilitate workflow prediction and AI-driven process mining enhances operational efficiency. Special attention is given to the role of AI orchestration tools in bottleneck reduction and workflow optimization. The article further explores blockchain implementation for secure workflow tracking and hyper-automation support, complemented by cloud-native architectural innovations. The findings demonstrate that integrating these emerging technologies significantly enhances workflow optimization, improves process transparency, and strengthens operational security. The article contributes to the growing body of knowledge on business process automation by providing a comprehensive framework for technology integration while highlighting current trends and future directions in workflow optimization. These insights offer valuable implications for businesses seeking to modernize their operational processes through emerging technologies.
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Carpenter, Chris. "Rig Automation Empowers Well Construction in Ecuador." Journal of Petroleum Technology 76, no. 02 (2024): 52–54. http://dx.doi.org/10.2118/0224-0052-jpt.
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_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 216249, “Digital Innovation of Well-Construction Process in Ecuador Through Rig Automation,” by Karen Peña, Kevin Etcheverry, and Hugo Quevedo, SPE, SLB, et al. The paper has not been peer reviewed. _ Artificial intelligence (AI)-based digital drilling technology was implemented in two mature fields in Ecuador that represent 33% of the country’s oil production. The drilling campaign’s main strategy included the deployment of a novel automation solution on two rigs, resulting in the optimization of the well-construction process. In the complete paper, the authors present the results of implementation of a rig-automation solution applied to 20 wells in 2022. Scope of the Digital Solution The authors detail the validation, optimization, and implementation of a drilling software to automate work flows and the installation and commissioning of associated hardware components. The following key indicators and objectives were established: - Validation of drilling-software work flows - Achievement of a 75% average in automation control - Improvement of rate of penetration (ROP) by 5% - Reduction of pre- and post-connection times by at least 30% - Real-time tuning of parameters - Training of all personnel involved in the use of the drilling-software package - Installation and commission of hardware components Drilling-Software Implementation The digital solution is an advanced software system that brings automation capabilities to drilling rigs, analogous to the different levels of automation found in automobiles. By drawing a parallel to car automation, one can better understand the system’s functionality and benefits. Level 1 consists of task automation, comparable to automatic driving features such as cruise control. In this context, the drilling software takes on the role of automating specific tasks within the drilling process. Level 2 involves process automation, similar to a car’s ability to autonomously park itself. The digital solution automates selected processes in drilling operations that enable efficient and precise execution. Level 3 encompasses adaptive automation, akin to a self-driving car that dynamically adapts to its surroundings. The drilling software continuously analyzes the real-time conditions of the wellbore and suggests optimal drilling parameters based on proprietary algorithms that leverage the well-known DeTournay model principle. The drilling software actively controls four key rig machines: the drawworks, topdrive, automated driller, and mud pumps. The solution adjusts drilling parameters intelligently, optimizing the drilling process and improving overall efficiency and drilling stability.
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.K, Sribatrinath. "A Study on Process Optimization in Wallet Production." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43124.
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Process optimization in wallet production is crucial for improving efficiency, reducing costs, and maintaining high-quality standards, especially in the highly competitive fashion and accessories industry, where manufacturers constantly strive to enhance their production processes, minimize waste, and maximize productivity to stay ahead in the market. As consumer demands increase and sustainability becomes a key concern, manufacturers must focus on identifying critical areas of improvement within the production cycle, such as material usage, labor efficiency, and technological advancements, to ensure smooth and cost-effective operations. This study delves into the importance of process optimization by exploring various advanced technologies, automation techniques, and lean manufacturing principles that can help streamline production, reduce unnecessary expenditures, and improve overall operational effectiveness. By adopting lean manufacturing methods, companies can eliminate inefficiencies, enhance workflow, and optimize resource allocation, leading to a more sustainable and profitable manufacturing process. Furthermore, optimizing production not only benefits manufacturers by lowering costs and increasing output but also ensures that customers receive high-quality products that meet industry standards and expectations, thereby strengthening brand reputation and customer satisfaction. The research aims to provide valuable insights into effective process optimization strategies, enabling manufacturers to implement best practices that drive innovation, sustainability, and long-term success in wallet production. Keywords: Wallet production, process optimization, lean manufacturing, cost efficiency, quality improvement, sustainability, supply chain management, automation, productivity enhancement, waste reduction.
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Sai, Kalyana Pranitha Buddiga. "Streamlining Operations: Advancements in Workflow Automation and Pipeline Optimization." European Journal of Advances in Engineering and Technology 9, no. 6 (2022): 63–65. https://doi.org/10.5281/zenodo.11213731.
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This white paper delves into the significance of workflow automation and pipeline management in modern business environments. With the increasing complexity of operations across industries, organizations are turning to automation to streamline processes, enhance efficiency, and reduce costs. By implementing robust workflow automation systems and effective pipeline management strategies, businesses can optimize resource utilization, accelerate time-to-market, and improve overall productivity. This paper explores key concepts, latest advancements, and emerging trends in workflow automation and pipeline management, providing insights into how organizations can leverage technology to drive operational excellence and achieve strategic objectives.
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Adenuga, Toluwanimi, and Francess Chinyere Okolo. "Automating Operational Processes as a Precursor to Intelligent, Self-Learning Business Systems." Journal of Frontiers in Multidisciplinary Research 2, no. 1 (2021): 133–47. https://doi.org/10.54660/.jfmr.2021.2.1.133-147.
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Automating operational processes is a critical precursor to realizing intelligent, self-learning business systems. This paper investigates the foundational role that process automation plays in enabling organizations to transition from rule-based task execution to adaptive, cognitive operations. By systematically automating repetitive tasks, data collection procedures, and decision workflows, enterprises lay the groundwork for higher-order artificial intelligence (AI) applications such as pattern recognition, real-time optimization, and dynamic decision-making. The transition to self-learning systems requires structured, high-quality data and streamlined workflows both of which are direct outcomes of automation. As organizations increasingly digitize operations, automation acts as both an efficiency enhancer and a data enabler, allowing AI systems to learn from consistent process outputs and improve over time. Through a series of cross-industry case studies, the paper illustrates how early-stage automation initiatives have evolved into intelligent platforms capable of contextual reasoning and autonomous decision execution. In manufacturing, robotic process automation (RPA) combined with IoT sensors has advanced from monitoring production lines to predicting equipment failure and optimizing supply levels. In the supply chain sector, automated logistics systems have matured into AI-powered networks that reroute shipments based on real-time disruptions and demand fluctuations. Within financial services, automation of customer onboarding, fraud detection, and compliance tracking has led to the development of cognitive platforms that personalize services and detect anomalies with minimal human input. These transformations are not merely technological but strategic demonstrating that operational automation is not an end in itself, but a stepping stone to building resilient, intelligent enterprises. The findings support the argument that businesses seeking to leverage AI at scale must begin by systematically automating foundational processes to ensure scalability, accuracy, and learning capacity. The paper concludes by emphasizing the strategic importance of automation in creating intelligent ecosystems that self-optimize, self-correct, and continuously evolve in response to internal and external variables.
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Rieck, Steffi, Joachim Heidelbach, and Tobias Stahl. "End-to-End- Prozessautomatisierung/End-to-end process automation." wt Werkstattstechnik online 113, no. 01-02 (2023): 42–47. http://dx.doi.org/10.37544/1436-4980-2023-01-02-46.
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Durchgängige, automatisierte Prozesse können Prozesskosten um 30 bis 40 Prozent senken und Durchlaufzeiten signifikant verkürzen. Der vorliegende Beitrag zeigt anhand eines siebenstufigen Vorgehens, wie automatisierte Prozesse umgesetzt werden. Dazu werden die Stufen (wie Aufnahme, Optimierung, Automatisierung und Monitoring) inklusive möglicher Methoden und Technologien dargestellt. Neben Robotic Process Automation (RPA) kommen hierbei Process Mining und Methoden der künstlichen Intelligenz (KI) zum Einsatz. Continuous, automated processes allow for reducing process costs by 30 to 40 percent and for significantly shortening lead times. A seven-stage approach shows how to put automated processes into practice. This paper presents the individual stages (such as recording, optimization, automation, and monitoring), including possible methods and technologies. To automate processes, Robotic Process Automation (RPA), process mining, and artificial intelligence (AI) technologies are used.
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Dewald, M., O. Kohn, Y. Dehorn, et al. "USING GAMIFICATION ON THE SHOP FLOOR FOR PROCESS OPTIMIZATION IN MACHINING PRODUCTION." MM Science Journal 2021, no. 5 (2021): 5154–59. http://dx.doi.org/10.17973/mmsj.2021_11_2021172.
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The optimization of production processes is even in 2021 still an up-to-date topic, where the use of data is discussed, and the utilization has proven to be effective in specific applications in the last 10 years. Despite increasing access to information directly from the production process and more available computing resources, the data still holds unused potential. Even ongoing digitalization does not replace a deep understanding of the underlying processes and a focus on online automation misses the potential including expertise of shop floor employees. Thus, besides automation and data-driven improvements it is important to support employees with innovative methods to exploit the full potential of the Internet of Things. Principles of gamification offer an opportunity to incorporate the shop floor personnel, to take their full attention on optimization of the process supporting them in their day-to-day job. Appropriate visualization creates incentives for continuous process optimization.
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Sakhno, G. O. "Robotic process automation (rpa) in retail: obvious and non-obvious benefits." Upravlenie kachestvom (Quality management), no. 3 (February 10, 2025): 20–21. https://doi.org/10.33920/pro-01-2503-04.
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The article discusses the implementation of robotic process automation (RPA) technology in retail, its impact on work effi ciency and customer service quality. Both obvious and non-obvious advantages of using RPA are described, including automation of routine tasks, optimization of logistics and improvement of customer service. The author emphasizes that the implementation of RPA allows you to focus human resources on more important tasks, which in turn can lead to increased sales and improved reputation of the company.
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Nathany, Deepika. "Supply Chain Automation: A Path to Operational Excellence and Sustainability." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 05, no. 04 (2021): 1–9. https://doi.org/10.55041/ijsrem8770.
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Supply chain automation represents an essential approach for attaining operational excellence while strengthening resilience and sustainability across global logistics. The paper integrates research from over 100 peer-reviewed studies and industry reports published through 2020 to explore how automation technologies are applied across supply chain functions along with their benefits and challenges. Automation includes multiple technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and the Internet of Things (IoT). The implementation of these technologies extends to warehouse management along with inventory control, demand forecasting, logistics optimization and additional essential sectors as identified by Atzori et al., 2017 and Choi et al., 2018 and Fosso Wamba et al., 2018. Studies on automated systems demonstrate enhancements in operations through productivity increases of 20% to 30% and substantial cost savings (Frank et al., 2019; Lacity & Willcocks, 2016). The implementation of AI-driven demand forecasting methods can cut forecast errors down by 50%, which leads to better inventory management and lower carrying costs (Tiwari et al., 2019). The integration of automated warehouse systems with autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) results in faster and more precise order fulfillment while cutting labor costs according to Zhong et al. (2017). Automation adoption faces obstacles such as significant upfront costs to implement systems and challenges integrating new solutions with existing legacy infrastructures while requiring specialized technical expertise (Strandhagen et al., 2017). Ethical issues surrounding AI decision-making in supply chains and job displacement potential continue to gain recognition as important concerns (Ghobakhloo, 2018). The research explores the role of automation in developing resilient supply chains which have gained significance after global disruptions like COVID-19 (Ivanov & Dolgui, 2020). Through their findings researchers, practitioners and policymakers can gain important information about supply chain automation's benefits and challenges which forms a basis for well- informed technology adoption strategies. Keywords Supply Chain Automation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Internet of Things, Warehouse Automation, Demand Forecasting, Logistics Optimization, Supply Chain Resilience
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Paszczuk, Michael. "Water Jet Automation." International Journal of Emerging Technology and Advanced Engineering 11, no. 10 (2021): 177–81. http://dx.doi.org/10.46338/ijetae1021_21.
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Water jet cutting has been an extremely helpful tool that creates flawless parts with tolerances up to 0.1 mm. During the cutting process, it is important to note that each step must be optimized to create the best finish or maintain the correct tolerance zone. These steps are composed of abrasive mass flow rate, traverse speed, and standoff distance. In order for these optimization techniques to be followed a strict set of rules must be followed to ensure consistent progression. Programs such as MATLAB can be utilized to reduce human error in the calculations. MATLAB files can then be saved to use with other materials and thickness combinations.
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Gutierrez Sullca, Erika Mirella. "Impact of software testing automation on the development cycle." Revista de Investigación Científica Huamachuco 1, no. 1 (2023): 23–27. http://dx.doi.org/10.61709/huamachuco.v1i1.3.
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Software development is a complex process, from its beginnings with the collection of requirements, processes are proposed for its development in order to achieve a quality product that manages to satisfy the user. Automation appears in the context of software testing development, a critical point in software development, since the quality of development depends on it. Automating software testing offers benefits in efficiency, resource optimization and product quality, also influencing the satisfaction of the development team. In conclusion, the implementation of tools that automate the software testing process will improve the software development cycle, provided they are implemented appropriately.
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Ghanshyambhai Patel, Samik. "Predictive Operation & Defensive Action (PrODA) for Intelligent Process Automation (IPA)." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46841.
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Abstract - Industrial Process Automation (IPA) is being progressively integrated into numerous processes to enhance productivity, reliability, quality and cost optimization in all aspects. Currently, IPA is focusing on real time process control only even though system is upgraded with highspeed controllers, improved resolution time stamping, historian & other next Gen technology. With technological advancement, IPA can be developed to next generation so that prediction of process abnormality, control failure time to Alarm & Trip, Root Cause, Suggestion for Defensive Action etc. can be explored. Researchers, Designers Technocrats will get new insights Predictive Operation & Defensive Action (PrODA) for IPA. Key Words: Intelligent Process Automation (IPA), Predictive Operation & Defensive Action (PrODA), Process Control, Automation
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Oravec, Juraj, Martin Kalúz, Peter Bakárač, and Monika Bakošová. "Improvements of Educational Process of Automation and Optimization Using 2D Plotter." IFAC-PapersOnLine 49, no. 6 (2016): 16–21. http://dx.doi.org/10.1016/j.ifacol.2016.07.146.
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Kurylev, R. A., A. P. Pavlov, and V. M. Semenov. "AUTOMATION OF THE PROCESS AND OPTIMIZATION OF TECHNICAL DIAGNOSTICS RESULTS PROCESSING." Problems of Gathering Treatment and Transportation of Oil and Oil Products, no. 1 (March 5, 2024): 183–91. http://dx.doi.org/10.17122/ntj-oil-2024-1-183-191.
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High safety requirements are imposed on the operation of production facilities. One of the requirements is periodic diagnostics of equipment and structures. Technical diagnostics is the recognition of the state of a technical system, which includes a wide rangeof problems associated with obtaining and evaluating diagnostic information. Surveys provide extensive information about the diagnostic object, which allows us to conclude about the operability of the object, to make a decision about repairs or modernization.The article discusses the process of diagnostics, its shortcomings and methods of their elimination. A program for automating the process and processing the results of technical diagnostics is proposed. The implementation of the development is aimed at increasing the reliability of the data obtained, reducing labor costs, and improving the state of safety at production facilities. It is also assumed that when using the development, the number of industrial accidents that occurred due to human factor errors during technical diagnostics will be reduced.The program consists of four elements, which will allow them to be gradually implemented in production: a sensor-server commu-nication system, a database, an electronic technological scheme of the object, a computing module. The communication system will simplify the transmission of data from the control sites. The database will facilitate work with documentation for control objects. The information available on the electronic technological scheme will increase the speed of interaction of employees of various services during diagnostics. The calculation module will eliminate the human factor during calculations and accelerate the formation of control acts and the conclusion of the industrial safety expertise.The joint use of all elements of the program will facilitate and accelerate the work on technical diagnostics and simplify the work with documentation for research objects.
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Kazarinov, L. S., and T. A. Barbasova. "Identification Method of Blast-Furnace Process Parameters." Key Engineering Materials 685 (February 2016): 137–41. http://dx.doi.org/10.4028/www.scientific.net/kem.685.137.
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This work considers performance identification by operation statistics aimed to mathematical descriptions of blast-furnace processes. In case of the blast-furnace process automation the operational task of process conditions optimization shall preferably be implemented based on the algorithm of performance identification by operation statistics alongside with control and stabilization of process conditions.
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Zhang, Miaomiao. "Numerical Analysis and Optimization of Heat Dissipation of Mechanical Automation Equipment Based on Thermal Model." International Journal of Heat and Technology 40, no. 1 (2022): 311–18. http://dx.doi.org/10.18280/ijht.400137.
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If the internal heat of mechanical automation equipment exceeds or does not reach the thermal equilibrium temperature range, it will adversely affect the operational reliability, environmental protection and production efficiency of the equipment. To tackle the problem, there has been some research on the internal heat dissipation of mechanical automation equipment, but mostly of the existing studies have simply aimed to change the parameters of the cooling system, and little has been done on the loading process and implementation details of the thermal model. Therefore, this paper provides numerical analysis and optimization of the space heat dissipation of mechanical automation equipment based on a thermal model. First, the heat dissipation mechanism of mechanical automation equipment was elaborated in detail, and the structure of the cooling system for mechanical automation equipment was given. Then, a spatial thermal model of mechanical automation equipment was established, and the heat dissipation design process of mechanical automation equipment was given. After that, the differences between the natural convection heat dissipation in large space and that in confined space inside mechanical automation equipment were explored. The experimental results verified the effectiveness of the numerical simulation model proposed in this paper.