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Journal articles on the topic 'Real-Time Process Optimization'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Bybee, Karen. "Real-Time Drilling-Process Optimization." Journal of Petroleum Technology 61, no. 08 (2009): 59–60. http://dx.doi.org/10.2118/0809-0059-jpt.

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Ajayi, Rhoda. "Integrating IoT and cloud computing for continuous process optimization in real time systems." International Journal of Research Publication and Reviews 6, no. 1 (2025): 2540–58. https://doi.org/10.55248/gengpi.6.0125.0441.

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Zhijiang, Shao, Wang Jinlin, and Qian Jixin. "Real-time Optimization of Acetaldehyde Production Process." Developments in Chemical Engineering and Mineral Processing 13, no. 3-4 (2008): 249–58. http://dx.doi.org/10.1002/apj.5500130306.

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Limanond, Suttipan, and Jennie Si. "RIE process time optimization from real time sensory data." IFAC Proceedings Volumes 32, no. 2 (1999): 443–48. http://dx.doi.org/10.1016/s1474-6670(17)56076-0.

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5

Miletic, I. P., and T. E. Marlin. "Results diagnosis for real-time process operations optimization." Computers & Chemical Engineering 22 (March 1998): S475—S482. http://dx.doi.org/10.1016/s0098-1354(98)00090-8.

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Boss, Edinara Adelaide, Rubens Maciel Filho, and Eduardo Coselli Vasco de Toledo. "Freeze drying process: real time model and optimization." Chemical Engineering and Processing: Process Intensification 43, no. 12 (2004): 1475–85. http://dx.doi.org/10.1016/j.cep.2004.01.005.

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7

Li, Dequn, Huamin Zhou, Peng Zhao, and Yang Li. "A real-time process optimization system for injection molding." Polymer Engineering & Science 49, no. 10 (2009): 2031–40. http://dx.doi.org/10.1002/pen.21444.

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8

Brooks, J. W. "Real-Time Data Capture Supports Process Modelling." Measurement and Control 35, no. 3 (2002): 69–71. http://dx.doi.org/10.1177/002029400203500302.

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This paper considers the real-time measurement and logging of physical variables from the forging process to enhance modelling of that process. The measurement problem is acute since environmental conditions are not ideal. The logging system is described and an approach is presented for the generation of accurate flow stress data. Improvements upon earlier efforts are discussed in the light of experience with modelling packages. The importance of accurate data for materials and machinery are highlighted.

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Huang, Jingwen, and Hongguang Li. "Plant-Wide Process Real-Time Optimization Based on Process Goose Queue Methodology." Procedia Engineering 29 (2012): 2526–31. http://dx.doi.org/10.1016/j.proeng.2012.01.345.

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Jeang, A., C.-C. Tsai, and C.-P. Chung. "Scheme for real-time process planning under process deterioration." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 223, no. 9 (2009): 1181–92. http://dx.doi.org/10.1243/09544054jem1356.

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Conventional approaches for process planning of manufacturing operations preset fixed process means and process tolerances for all operations and permit outputs to be distributed around these fixed values, provided that the final outputs meet acceptable specifications. These approaches are inappropriate for high-value and precision manufacturing processes, especially those where a complex part may suffer from process deterioration. The problem is that in these approaches, process means and process tolerances are considered to be independent decision variables, whereby the resultant process mea

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Sasikala, D., and K. Venkatesh Sharma. "Optimization of Automated Real-Time Dashboards." Journal of Electronics and Informatics 4, no. 2 (2022): 114–21. http://dx.doi.org/10.36548/jei.2022.2.006.

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A dashboard is a powerful tool that enables users to verify, analyze, and process all kinds of data. It represents information on a single screen that helps get it just whenever it is essential and achieve the set goals in time. The category of a dashboard varies due to the nature of data they signify and the drive of the corporate they are castoff by. To blueprint a dashboard that will encounter the entire necessities and wholly accomplishing its targets, custom the ideologies of worthy dashboard sketch. But functioning on the project task, continually ponder its objective and the gain of the

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12

Cubillos, F. A., G. Acuña, and E. L. Lima. "Real-time process optimization based on grey-box neural models." Brazilian Journal of Chemical Engineering 24, no. 3 (2007): 433–43. http://dx.doi.org/10.1590/s0104-66322007000300012.

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MESSAC, ACHILLE, and XUAN CHEN. "VISUALIZING THE OPTIMIZATION PROCESS IN REAL-TIME USING PHYSICAL PROGRAMMING." Engineering Optimization 32, no. 6 (2000): 721–47. http://dx.doi.org/10.1080/03052150008941319.

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Erik Ydstie, B., and Duncan P. Coffey. "Distributed Control and Real-Time Optimization of a Chemical Process." IFAC Proceedings Volumes 31, no. 11 (1998): 691–96. http://dx.doi.org/10.1016/s1474-6670(17)45006-3.

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15

Kong, Lingshuang, Yanyan Yin, Chunhua Yang, Weihua Gui, and Kok Lay Teo. "Stochastic optimization for real-time operation of alumina blending process." Journal of Process Control 96 (December 2020): 49–56. http://dx.doi.org/10.1016/j.jprocont.2020.10.005.

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16

Naysmith, M. R., and P. L. Douglas. "Review of Real Time Optimization in the Chemical Process Industries." Developments in Chemical Engineering and Mineral Processing 3, no. 2 (2008): 67–87. http://dx.doi.org/10.1002/apj.5500030202.

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17

Thierry, David, and Lorenz T. Biegler. "Dynamic real‐time optimization for a CO 2 capture process." AIChE Journal 65, no. 7 (2019): e16511. http://dx.doi.org/10.1002/aic.16511.

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18

Kumar, Devineni Gireesh, Aman Ganesh, Neerudi Bhoopal, et al. "Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review." Ingénierie des systèmes d information 26, no. 2 (2021): 179–90. http://dx.doi.org/10.18280/isi.260205.

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This paper presents a variety of contemporary optimization techniques inspired by the real life in nature. Optimization reveals substantial developments in computing systems as well as has come to be the most encouraging strategy for several design applications. The study is conducted on single-objective, multi-objective, and hybrid optimization strategies. These optimization schemes will be of excellent help to organizations to identify optimum criteria and to improve process as well as product high quality. For selected optimization strategies, the process of formulating the objective functi

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Kelkar, A. S., R. L. Mahajan, and R. L. Sani. "Real-Time Physiconeural Solutions for MOCVD." Journal of Heat Transfer 118, no. 4 (1996): 814–21. http://dx.doi.org/10.1115/1.2822575.

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This paper presents an integrated physiconeural network approach for the modeling and optimization of a vertical MOCVD reactor. The basic concept is to utilize the solutions obtained from a physical model to build an accurate neural network (NN) model The resulting model has the attractive features of self-adaptiveness and speed of prediction and is an ideal starting tool for process optimization and control. Following this approach, a first-principles physical model for the reactor was solved numerically using the Fluid Dynamics Analysis Package (FIDAP). This transient model included property

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20

Niemczyk, Thomas M., Garth W. Gobeli, and Michael G. Harmon. "Real time optimization of spectrometer scan rate." Chemometrics and Intelligent Laboratory Systems 7, no. 3 (1990): 295–302. http://dx.doi.org/10.1016/0169-7439(90)80118-p.

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21

Kane, Russell D., Dawn C. Eden, and David A. Eden. "Real-Time Solutions Integrate Corrosion Monitoring with Process Control." Materials Performance 44, no. 2 (2005): 36–41. https://doi.org/10.5006/mp2005_44_2-36.

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Online, real-time technologies have increased the accuracy of data and their relevance and value to the goal of increasing productivity by reducing corrosion damage, failures, and unplanned outages, thus decreasing downtime and increasing run time. This article examines methods currently available for field and in-plant monitoring of general and localized corrosion. Data can easily integrate with existing process-control and asset-management systems. Examples of vapor phase monitoring, process optimization, monitoring microbially active water-handling systems, and real-time inhibition control

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22

Li, Hongguang, and Jingwen Huang. "Process Goose Queue (PGQ) Approaches toward Plantwide Process Optimization with Applications in Supervision-Driven Real-Time Optimization." Industrial & Engineering Chemistry Research 51, no. 33 (2012): 10848–59. http://dx.doi.org/10.1021/ie300374g.

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23

Torres Zúñiga, Ixbalank, Daniel Navia, Fernando López-Caamal, and Héctor Hernández-Escoto. "Real-Time Optimization with Modifier Adaptation Based on Dynamic Gradient Estimation." Memorias del Congreso Nacional de Control Automático 6, no. 1 (2023): 145–50. http://dx.doi.org/10.58571/cnca.amca.2023.006.

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In this paper, an optimization strategy with modifier adaptation based on a mechanistic model and the estimation of the input-output map gradient is presented to solve optimization problems in real-time. The strategy consists of defining an optimization problem based on the process model with first-order modifiers in the objective function that include the information of the input-output map gradient to correct the uncertainties of the process model. The gradient of the input-output map is estimated by means of a differentiator based on a multivariable Super-Twisting algorithm. The proposed re

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24

Quah, Titus, Derek Machalek, and Kody M. Powell. "Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process." Processes 8, no. 11 (2020): 1497. http://dx.doi.org/10.3390/pr8111497.

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles mode

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25

Xiong, ZHAO, ZHENG Lianyu, FAN Wei, and YU Lu. "Real-time Machining Vibration Data Driven Milling Process Parameters Adaptive Optimization." Journal of Mechanical Engineering 56, no. 23 (2020): 172. http://dx.doi.org/10.3901/jme.2020.23.172.

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26

Jun, Zhang, Mao Zhi-zhong, and Jia Run-da. "Real-time optimization based on SCFO for gold cyanidation leaching process." Chemical Engineering Science 134 (September 2015): 467–76. http://dx.doi.org/10.1016/j.ces.2015.05.020.

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27

Howes, S., I. Mohler, and N. Bolf. "Real-time dynamic process control loop identification, tuning and optimization software." IFAC-PapersOnLine 49, no. 6 (2016): 213–17. http://dx.doi.org/10.1016/j.ifacol.2016.07.179.

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28

Speakman, Jack, and Grégory François. "A Multiple Solution Approach to Real-Time Optimization." Processes 10, no. 11 (2022): 2207. http://dx.doi.org/10.3390/pr10112207.

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Modifier Adaptation (MA) is a method of real-time optimization (RTO) which modifies a single model to match the first order properties of the plant. Known uncertainties in the parameters of this model are discarded in favor of real-time measurements, but they can be used to quantify the mismatch between the plant and model. Using multi-model methods increases the computation time, but can improve rate of convergence of the RTO scheme. This article proposes a framework, known as multiple solution modifier adaptation (MSMA), which produces several models which are all modified in the same way as

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29

Gottu Mukkula, Anwesh Reddy, and Sebastian Engell. "Handling Measurement Delay in Iterative Real-Time Optimization Methods." Processes 9, no. 10 (2021): 1800. http://dx.doi.org/10.3390/pr9101800.

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This paper is concerned with the real-time optimization (RTO) of chemical plants, i.e., the optimization of the steady-state operating points during operation, based on inaccurate models. Specifically, modifier adaptation is employed to cope with the plant-model mismatch, which corrects the plant model and the constraint functions by bias and gradient correction terms that are computed from measured variables at the steady-states of the plant. This implies that the sampling time of the iterative RTO scheme is lower-bounded by the time to reach a new steady-state after the previously computed i

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30

Trusty, Andrew, Santiago Santiago Ontañón, and Ashwin Ram. "Stochastic Plan Optimization in Real-Time Strategy Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 4, no. 1 (2021): 126–31. http://dx.doi.org/10.1609/aiide.v4i1.18684.

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We present a domain independent off-line adaptation technique called Stochastic Plan Optimization for finding and improving plans in real-time strategy games. Our method is based on ideas from genetic algorithms but we utilize a different representation for our plans and an alternate initialization procedure for our search process. The key to our technique is the use of expert plans to initialize our search in the most relevant parts of plan space. Our experiments validate this approach using our existing case based reasoning system Darmok in the real-time strategy game Wargus, a clone of Warc

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31

Vilas, Carlos, Antonio A. Alonso, Eva Balsa-Canto, Estefanía López-Quiroga, and Ioan Cristian Trelea. "Model-Based Real Time Operation of the Freeze-Drying Process." Processes 8, no. 3 (2020): 325. http://dx.doi.org/10.3390/pr8030325.

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Background: Freeze-drying or lyophilization is a dehydration process employed in high added-value food and biochemical goods. It helps to maintain product organoleptic and nutritional properties. The proper handling of the product temperature during the operation is critical to preserve quality and to reduce the process duration. Methods: Mathematical models are useful tools that can be used to design optimal policies that minimize production costs while keeping product quality. In this work, we derive an operational mathematical model to describe product quality and stability during the freez

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32

Saucedo, Victor M., and M. Nazmul Karim. "Experimental optimization of a real time fed-batch fermentation process using Markov decision process." Biotechnology and Bioengineering 55, no. 2 (1997): 317–27. http://dx.doi.org/10.1002/(sici)1097-0290(19970720)55:2<317::aid-bit9>3.0.co;2-l.

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Diao, Yan Fang, Jie Dong, Gang Wang, Na Yao, and Fan Ye. "Reservoir Real-Time Optimization Operation Model Based on PSO." Advanced Materials Research 518-523 (May 2012): 3676–79. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.3676.

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The reservoir real-time operation is a cyclic process of forcast-decision making-implementation, in which the key issue is to confirm discharges quickly and exactly. In order to solve this issue, a reservoir real-time optimization operation model based on Particle Swarm Optimization (PSO) is proposed and the real-time operation procedure is illustrated in this paper. Taking the flood hydrograph 19750729 of Huanren reservoir as an example, discharges calculated by the real-time optimization operation model are compared with those calculated by conventional operation. It could be seen that the r

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34

Krishnamoorthy, Dinesh. "ECCBO: An inherently safe Bayesian optimization with embedded constraint control for real-time process optimization." Journal of Process Control 152 (August 2025): 103467. https://doi.org/10.1016/j.jprocont.2025.103467.

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35

Faria, Ruan de Rezende, Bruno Didier Olivier Capron, Maurício B. de Souza Jr., and Argimiro Resende Secchi. "One-Layer Real-Time Optimization Using Reinforcement Learning: A Review with Guidelines." Processes 11, no. 1 (2023): 123. http://dx.doi.org/10.3390/pr11010123.

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This paper reviews real-time optimization from a reinforcement learning point of view. The typical control and optimization system hierarchy depend on the layers of real-time optimization, supervisory control, and regulatory control. The literature about each mentioned layer is reviewed, supporting the proposal of a benchmark study of reinforcement learning using a one-layer approach. The multi-agent deep deterministic policy gradient algorithm was applied for economic optimization and control of the isothermal Van de Vusse reactor. The cooperative control agents allowed obtaining sufficiently

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Haq, Ahteshamul, Babji Srinivasan, and Dominique Bonvin. "Real-Time Optimization of Wastewater Treatment Plants via Constraint Adaptation." Processes 10, no. 5 (2022): 990. http://dx.doi.org/10.3390/pr10050990.

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An important requirement of wastewater treatment plants (WWTPs) is compliance with the local regulations on effluent discharge, which are going to become more stringent in the future. The operation of WWTPs exhibits a trade-off between operational cost and effluent quality, which provides a scope for optimization. Process optimization is usually done by optimizing a model of the process. However, due to inevitable plant–model mismatch, the computed optimal solution is usually not optimal for the plant. This study represents the first attempt to handle plant–model mismatch via constraint adapta

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37

Buttz, J., G. Daigger, and M. Robbins. "Real-Time Biological Process Optimization Using Off-Line and On-Line Monitoring." Water Science and Technology 22, no. 7-8 (1990): 317–18. http://dx.doi.org/10.2166/wst.1990.0276.

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38

Choi, Seungjune, Seunghyok Kim, and En Sup Yoon. "Evolutionary Improvement Algorithm with Statistical Learning Method for Process Real-Time Optimization." IFAC Proceedings Volumes 41, no. 2 (2008): 3258–62. http://dx.doi.org/10.3182/20080706-5-kr-1001.00553.

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39

Kumar, Vivek, and Nitin Kaistha. "Real-Time Optimization of a Reactor–Separator–Recycle Process II: Dynamic Evaluation." Industrial & Engineering Chemistry Research 58, no. 5 (2019): 1966–77. http://dx.doi.org/10.1021/acs.iecr.8b04274.

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40

Lei, Wei, Laurent Henn-Lecordier, Mariano Anderle, Gary W. Rubloff, Mario Barozzi, and Massimo Bersani. "Real-time observation and optimization of tungsten atomic layer deposition process cycle." Journal of Vacuum Science & Technology B: Microelectronics and Nanometer Structures 24, no. 2 (2006): 780. http://dx.doi.org/10.1116/1.2184320.

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41

Devan, P. Arun Mozhi, Fawnizu Azmadi Hussin, Rosdiazli B. Ibrahim, Kishore Bingi, M. Nagarajapandian, and Maher Assaad. "An Arithmetic-Trigonometric Optimization Algorithm with Application for Control of Real-Time Pressure Process Plant." Sensors 22, no. 2 (2022): 617. http://dx.doi.org/10.3390/s22020617.

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This paper proposes a novel hybrid arithmetic–trigonometric optimization algorithm (ATOA) using different trigonometric functions for complex and continuously evolving real-time problems. The proposed algorithm adopts different trigonometric functions, namely sin, cos, and tan, with the conventional sine cosine algorithm (SCA) and arithmetic optimization algorithm (AOA) to improve the convergence rate and optimal search area in the exploration and exploitation phases. The proposed algorithm is simulated with 33 distinct optimization test problems consisting of multiple dimensions to showcase t

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42

Yang, Teng, Ying Feng Zhang, and Jun Qiang Wang. "Intelligent Navigation of Assembly Stations Based on Real-Time Manufacturing Information." Applied Mechanics and Materials 432 (September 2013): 442–46. http://dx.doi.org/10.4028/www.scientific.net/amm.432.442.

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In order to improve the real-time control and optimization of assembly process, this article takes the basic execution unitassembly station as the study subject. Based on real-time manufacturing information, the concept of the intelligent navigation of assembly activities is put forward. Through the application of RFID technology to capture the multi-source information, three kinds of navigation services, namely real-time assembly operating guidance service, collaborative manufacturing service among assembly stations and real-time optimization service of task queue, are designed to implement o

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Wang, Ying, Feifei Shi, and Bingbing Chen. "Real-Time Structure Generation Based on Data-Driven Using Machine Learning." Processes 11, no. 3 (2023): 802. http://dx.doi.org/10.3390/pr11030802.

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Topology optimization results are highly dependent on the given design constraints and boundary conditions. Moreover, small changes in initial design conditions can result in different topological configurations, which makes topology optimization time-consuming in a given design constraint domain and inefficient in structural design. To address this problem, a data-driven real-time topology optimization framework and method coupled with machine learning by using a principal component analysis algorithm combined with a feedforward neural network are developed in this paper. Meanwhile, through t

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Lee, Chi-Yuan, Chia-Hung Chen, Guo-Bin Jung, Yu-Xiang Zheng, and Yi-Cheng Liu. "PEMWE with Internal Real-Time Microscopic Monitoring Function." Membranes 11, no. 2 (2021): 92. http://dx.doi.org/10.3390/membranes11020092.

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In recent years, various countries have been paying attention to environmental protection issues, believing that climate change is the main challenge to the developed countries’ energy policies. The most discussed solution is renewable energy. The energy storage system can reduce the burden of the overall power system of renewable energy. The hydrogen energy is one of the optimal energy storage system options of renewable energy at present. According to these policies and the future trend, this study used micro-electro-mechanical systems (MEMS) technology to integrate micro voltage, current, t

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Zhang, Wei, Chun Hua Zhang, and Yan Zhang. "On Real-Time Modeling of 3D Terrain Database." Advanced Materials Research 760-762 (September 2013): 1884–87. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1884.

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With the uprising deployment of large-scale environmental simulations, as well as the wide applications of the GIS data in the simulation models, the traditional modeling methods have been difficult to meet the requirements of terrain generation of large areas. In this paper, we study a new terrain database modeling process based on Celerity/TerraVista, which uses the techniques of image compression, block optimization, and level of detail (LOD) , for terrain generation of large areas. Tis new modeling process has been applied in our independently developed simulation system and has achieved g

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46

Seo, Seungwoo, and Jong-Moon Chung. "Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems." IEEE Access 9 (2021): 156372–79. http://dx.doi.org/10.1109/access.2021.3129885.

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Yoon, Ho-Sub, Seong-Woo Park, and Jang-Hee Yoo. "Real-Time Hair Segmentation Using Mobile-Unet." Electronics 10, no. 2 (2021): 99. http://dx.doi.org/10.3390/electronics10020099.

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We described a real-time hair segmentation method based on a fully convolutional network with the basic structure of an encoder–decoder. In one of the traditional computer vision techniques for hair segmentation, the mean shift and watershed methodologies suffer from inaccuracy and slow execution due to multi-step, complex image processing. It is also difficult to execute the process in real-time unless an optimization technique is applied to the partition. To solve this problem, we exploited Mobile-Unet using the U-Net segmentation model, which incorporates the optimization techniques of Mobi

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48

Zhang, Zhihao, Zhe Wu, David Rincon, and Panagiotis Christofides. "Real-Time Optimization and Control of Nonlinear Processes Using Machine Learning." Mathematics 7, no. 10 (2019): 890. http://dx.doi.org/10.3390/math7100890.

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Machine learning has attracted extensive interest in the process engineering field, due to the capability of modeling complex nonlinear process behavior. This work presents a method for combining neural network models with first-principles models in real-time optimization (RTO) and model predictive control (MPC) and demonstrates the application to two chemical process examples. First, the proposed methodology that integrates a neural network model and a first-principles model in the optimization problems of RTO and MPC is discussed. Then, two chemical process examples are presented. In the fir

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Kushal Shah. "Real-Time Analytics in E-commerce: Strategies for Implementing Near Real-Time ETL Pipelines." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 2654–75. https://doi.org/10.32628/cseit25112746.

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Near real-time ETL pipelines have emerged as a transformative force in e-commerce, enabling organizations to process and analyze data within seconds of its creation rather than relying on traditional batch methods. This article explores the architectural components, implementation strategies, and business applications of these advanced data processing systems. By examining pipeline architecture—including ingestion layers, change data capture mechanisms, stream processing engines, and storage solutions—alongside various implementation approaches like micro-batch processing and lambda architectu

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Gazsó, Z., F. Házi, I. Kenyeres, and L. Váci. "Full-scale wastewater treatment plant simulation for real-time optimization." Water Practice and Technology 12, no. 4 (2017): 848–56. http://dx.doi.org/10.2166/wpt.2017.091.

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Abstract A dynamic simulation model has been developed and validated for the 1.4 million p.e. capacity Budapest Central Wastewater Treatment Plant to support intensification, process development and risk assessment. By the integration of both the biological and physico-chemical processes the technological design of separated units becomes possible as well as the exploration of the connections within the system. The calibration of the model parameters for an operating treatment plant is the key requirement for the proper application of dynamic simulation tool to optimize operational and mainten

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