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Journal articles on the topic 'Supervisory model predictive control'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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1

Tatjewski, Piotr. "Supervisory predictive control and on-line set-point optimization." International Journal of Applied Mathematics and Computer Science 20, no. 3 (September 1, 2010): 483–95. http://dx.doi.org/10.2478/v10006-010-0035-1.

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Supervisory predictive control and on-line set-point optimizationThe subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on

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Jin, Jionghua, Huairui Guo, and Shiyu Zhou. "Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes." Journal of Manufacturing Science and Engineering 128, no. 1 (December 15, 2004): 315–25. http://dx.doi.org/10.1115/1.2114912.

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This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise

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Meyer, Kristian, Thomas Bisgaard, Jakob K. Huusom, and Jens Abildskov. "Supervisory Model Predictive Control of the Heat Integrated Distillation Column." IFAC-PapersOnLine 50, no. 1 (July 2017): 7375–80. http://dx.doi.org/10.1016/j.ifacol.2017.08.1506.

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Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (November 20, 2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of pl

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Papangelis, Lampros, Marie-Sophie Debry, Patrick Panciatici, and Thierry Van Cutsem. "Coordinated Supervisory Control of Multi-Terminal HVDC Grids: A Model Predictive Control Approach." IEEE Transactions on Power Systems 32, no. 6 (November 2017): 4673–83. http://dx.doi.org/10.1109/tpwrs.2017.2659781.

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Creemers, Falco, Alejandro Ivan Morales Medina, Erjen Lefeber, and Nathan van de Wouw. "Design of a supervisory controller for Cooperative Intersection Control using Model Predictive Control." IFAC-PapersOnLine 51, no. 33 (2018): 74–79. http://dx.doi.org/10.1016/j.ifacol.2018.12.096.

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Li, Su Zhen, Xiang Jie Liu, and Gang Yuan. "Application of Supervisory Predictive Control Based on T-S Model in pH Neutralization Process." Applied Mechanics and Materials 511-512 (February 2014): 867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.867.

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T-S model is linearized at sampling points into the form of linear time-invariant state space , and using supervisory predictive control and muti-step predictive control strategy, which reduces amount of calculation and improves the control performance. Introduction

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Grosso, J. M., C. Ocampo-Martínez, and V. Puig. "Learning-based tuning of supervisory model predictive control for drinking water networks." Engineering Applications of Artificial Intelligence 26, no. 7 (August 2013): 1741–50. http://dx.doi.org/10.1016/j.engappai.2013.03.003.

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Rosen, C., M. Larsson, U. Jeppson, and Z. Yuan. "A framework for extreme-event control in wastewater treatment." Water Science and Technology 45, no. 4-5 (February 1, 2002): 299–308. http://dx.doi.org/10.2166/wst.2002.0610.

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In this paper an approach to extreme event control in wastewater treatment plant operation by use of automatic supervisory control is discussed. The framework presented is based on the fact that different operational conditions manifest themselves as clusters in a multivariate measurement space. These clusters are identified and linked to specific and corresponding events by use of principal component analysis and fuzzy c-means clustering. A reduced system model is assigned to each type of extreme event and used to calculate appropriate local controller set points. In earlier work we have show

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Morales-Rodelo, Keidy, Mario Francisco, Hernan Alvarez, Pastora Vega, and Silvana Revollar. "Collaborative Control Applied to BSM1 for Wastewater Treatment Plants." Processes 8, no. 11 (November 16, 2020): 1465. http://dx.doi.org/10.3390/pr8111465.

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This paper describes a design procedure for a collaborative control structure in Plant Wide Control (PWC), taking into account the existing controllable parameters as a novelty in the procedure. The collaborative control structure includes two layers, supervisory and regulatory, which are determined according to the dynamics hierarchy obtained by means of the Hankel matrix. The supervisory layer is determined by the main dynamics of the process and the regulatory layer comprises the secondary dynamics and controllable parameters. The methodology proposed is applied to a wastewater treatment pl

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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, Pablo de Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios Rovas, Simone Steiger, Christopher Mutschler, and Gunnar Gruen. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (December 2, 2018): 3376. http://dx.doi.org/10.3390/en11123376.

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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with

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Ouammi, Ahmed, Yasmine Achour, Driss Zejli, and Hanane Dagdougui. "Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid." IEEE Transactions on Automation Science and Engineering 17, no. 1 (January 2020): 117–28. http://dx.doi.org/10.1109/tase.2019.2910756.

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Flor Unda, Omar. "Adaptive control systems for solar collectors." Athenea 2, no. 4 (June 15, 2021): 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.

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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlin

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May-Ostendorp, Peter T., Gregor P. Henze, Balaji Rajagopalan, and Charles D. Corbin. "Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building." Journal of Building Performance Simulation 6, no. 3 (May 2013): 199–219. http://dx.doi.org/10.1080/19401493.2012.665481.

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15

Löhr, Yannik, Daniel Wolf, Clemens Pollerberg, Alexander Hörsting, and Martin Mönnigmann. "Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages." Applied Energy 290 (May 2021): 116742. http://dx.doi.org/10.1016/j.apenergy.2021.116742.

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Wagenpfeil, J., E. Arnold, H. Linke, and O. Sawodny. "Modelling and optimized water management of artificial inland waterway systems." Journal of Hydroinformatics 15, no. 2 (December 18, 2012): 348–65. http://dx.doi.org/10.2166/hydro.2012.163.

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A decision support system (DSS) for optimized operational water management of artificial inland waterways is presented. It will be deployed as part of a supervisory control and data acquisition (SCADA) system of the Mittellandkanal (MLK), a large canal structure in northern Germany, and relies on experience gained from a similar system. The DSS uses a model predictive controller with a 48 h prediction horizon to calculate optimal pump and discharge strategies that will ensure navigable water levels and at the same time minimize operational costs. The internal process model for the model predic

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Boussemart, Yves, and Mary L. Cummings. "Predictive models of human supervisory control behavioral patterns using hidden semi-Markov models." Engineering Applications of Artificial Intelligence 24, no. 7 (October 2011): 1252–62. http://dx.doi.org/10.1016/j.engappai.2011.04.008.

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Breslow, Leonard A., Daniel Gartenberg, J. Malcolm McCurry, and J. Gregory Trafton. "Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control." IEEE Transactions on Human-Machine Systems 44, no. 1 (February 2014): 30–40. http://dx.doi.org/10.1109/tsmc.2013.2293317.

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19

Ramamurthi, K., and C. L. Hough. "Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations." Journal of Engineering for Industry 115, no. 3 (August 1, 1993): 268–77. http://dx.doi.org/10.1115/1.2901660.

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Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base du

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Milla, Freddy, Manuel A. Duarte-Mermoud, and Noreys Aguila-Camacho. "Hierarchical MPC Secondary Control for Electric Power System." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/397567.

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Although in electric power systems (EPS) the regulatory level guarantees a bounded error between the reference and the corresponding system variables, to keep its availability in time, optimizing the system operation is required for operational reasons such as, economic and/or environmental. In order to do this, there are the following alternative solutions: first, replacing the regulatory system with an optimized control system or simply adding an optimized supervisory level, without modifying the regulatory level. However, due to the high cost associated with the modification of regulatory c

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Aboelhassan, Ahmed, M. Abdelgeliel, Ezz Eldin Zakzouk, and Michael Galea. "Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications." Energies 13, no. 24 (December 14, 2020): 6594. http://dx.doi.org/10.3390/en13246594.

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Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative

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22

Yahya, Olfa, Zeineb Lassoued, and Kamel Abderrahim. "Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model." International Journal of Automation and Computing 16, no. 5 (October 1, 2018): 683–95. http://dx.doi.org/10.1007/s11633-018-1148-5.

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23

Howland, Michael F., and John O. Dabiri. "Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation." Energies 14, no. 1 (December 24, 2020): 52. http://dx.doi.org/10.3390/en14010052.

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Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition m

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Ryu, Hyuncheol, and Jong Min Lee. "Model Predictive Control (MPC)-Based Supervisory Control and Design of Off-Gas Recovery Plant with Periodic Disturbances from Parallel Batch Reactors." Industrial & Engineering Chemistry Research 55, no. 11 (March 10, 2016): 3013–25. http://dx.doi.org/10.1021/acs.iecr.5b03224.

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25

Achour, Yasmine, Ahmed Ouammi, Driss Zejli, and Sami Sayadi. "Supervisory Model Predictive Control for Optimal Operation of a Greenhouse Indoor Environment Coping With Food-Energy-Water Nexus." IEEE Access 8 (2020): 211562–75. http://dx.doi.org/10.1109/access.2020.3037222.

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26

Rui, Yan Nian, Xiao Mei Jiang, and Kai Qiang Liu. "Intelligent Control in Low Pressure Methanol Carbonylation CH₃COOH Reaction." Advanced Materials Research 233-235 (May 2011): 1027–30. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.1027.

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With catalysis system, carbonylation of methanol liquid is a better method in manufacture of acetic acid. However previous control mode can not well meet technique demands. This paper made some modification on reaction process control based on manufacturing technique. Aimed at much influence upon temperature including uncertainty, cross coupling effects and big delay about model, predictive functional control (PFC) technique combined with PID and feedforward are applied to temperature control of reaction process through supervisory total distributed control which leads to the improvement of re

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Gwaivangmin, BI, and JD Jiya. "WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL." Nigerian Journal of Technology 36, no. 1 (December 29, 2016): 148–54. http://dx.doi.org/10.4314/njt.v36i1.19.

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With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network. Thus, water demand forecasting becomes necessary at the demand nodes. This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos. The proposed model will be developed based on h

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Lee, Hyunjin, Bruce A. Buckingham, Darrell M. Wilson, and B. Wayne Bequette. "A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator." Journal of Diabetes Science and Technology 3, no. 5 (September 2009): 1082–90. http://dx.doi.org/10.1177/193229680900300511.

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The objective of this article is to present a comprehensive strategy for a closed-loop artificial pancreas. A meal detection and meal size estimation algorithm is developed for situations in which the subject forgets to provide a meal insulin bolus. A pharmacodynamic model of insulin action is used to provide insulin-on-board constraints to explicitly include the future effect of past and currently delivered insulin boluses. In addition, a supervisory pump shut-off feature is presented to avoid hypoglycemia. All of these components are used in conjunction with a feedback control algorithm usin

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Adegbenro, Akinkunmi, Michael Short, and Claudio Angione. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications." Energies 14, no. 8 (April 8, 2021): 2078. http://dx.doi.org/10.3390/en14082078.

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Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) pri

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Marusak, Piotr, and Piotr Tatjewski. "Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point Optimizers." International Journal of Applied Mathematics and Computer Science 18, no. 4 (December 1, 2008): 539–52. http://dx.doi.org/10.2478/v10006-008-0047-2.

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Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point OptimizersMechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator bloc

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Anuntasethakul, Chanthawit, and David Banjerdpongchai. "Design of Supervisory Model Predictive Control for Building HVAC System With Consideration of Peak-Load Shaving and Thermal Comfort." IEEE Access 9 (2021): 41066–81. http://dx.doi.org/10.1109/access.2021.3065083.

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Duan, Xuechao, Yuanying Qiu, Jianwei Mi, and Ze Zhao. "Motion prediction and supervisory control of the macro–micro parallel manipulator system." Robotica 29, no. 7 (April 11, 2011): 1005–15. http://dx.doi.org/10.1017/s0263574711000282.

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SUMMARYThis paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a cano

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Franzè, Giuseppe, Angelo Furfaro, Massimiliano Mattei, and Valerio Scordamaglia. "A Safe Supervisory Flight Control Scheme in the Presence of Constraints and Anomalies." International Journal of Applied Mathematics and Computer Science 25, no. 1 (March 1, 2015): 39–51. http://dx.doi.org/10.1515/amcs-2015-0003.

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Abstract In this paper the hybrid supervisory control architecture developed by Famularo et al. (2011) for constrained control systems is adopted with the aim to improve safety in aircraft operations when critical events like command saturations or unpredicted anomalies occur. The capabilities of a low-computational demanding predictive scheme for the supervision of non-linear dynamical systems subject to sudden switchings amongst operating conditions and time-varying constraints are exploited in the flight control systems framework. The strategy is based on command governor ideas and is tailo

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Vilas Boas, Fernanda Mitchelly, Luiz Eduardo Borges-da-Silva, Helcio Francisco Villa-Nova, Erik Leandro Bonaldi, Levy Ely Lacerda Oliveira, Germano Lambert-Torres, Frederico de Oliveira Assuncao, et al. "Condition Monitoring of Internal Combustion Engines in Thermal Power Plants Based on Control Charts and Adapted Nelson Rules." Energies 14, no. 16 (August 11, 2021): 4924. http://dx.doi.org/10.3390/en14164924.

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In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on

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Aswad Mohammed, Subhi, Osama Ali Awad, and Abdulkareem Merhej Radhi. "Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1613. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1613-1625.

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This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, i

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Roscher, Björn, and Ralf Schelenz. "Usability of SCADA as predictive maintenance for wind turbines." Forschung im Ingenieurwesen 85, no. 2 (March 22, 2021): 173–80. http://dx.doi.org/10.1007/s10010-021-00454-1.

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AbstractWind energy is an essential source of renewable energy. However, to compete with conventional energy sources, energy needs to be produced at low costs. An ideal situation would be to have no costly, unscheduled maintenance, preferably. Currently, O&M are half of the yearly expenses. The O&M costs are kept low by scheduled and reactive maintenance. An alternative is predictive maintenance. This method aims to act before any critical and costly repair is required. Additionally, the component is used to its full potential. However, such a strategy requires a damage indication, sim

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Allen, James, Ari Halberstadt, John Powers, and Nael H. El-Farra. "An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management." Mathematics 8, no. 8 (July 23, 2020): 1215. http://dx.doi.org/10.3390/math8081215.

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This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV

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Iannace, Gino, Giuseppe Ciaburro, and Amelia Trematerra. "Wind Turbine Noise Prediction Using Random Forest Regression." Machines 7, no. 4 (November 6, 2019): 69. http://dx.doi.org/10.3390/machines7040069.

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Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been an

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Schwanenberg, D., B. P. J. Becker, and M. Xu. "The open real-time control (RTC)-Tools software framework for modeling RTC in water resources sytems." Journal of Hydroinformatics 17, no. 1 (September 9, 2014): 130–48. http://dx.doi.org/10.2166/hydro.2014.046.

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Real-time control-Tools is a novel software framework for modeling real-time control and decision support in water resources systems. It integrates different control paradigms ranging from simple feedback control strategies with triggers, operating rules and controllers to advanced optimization-based approaches such as model predictive control (MPC). A key feature of the package is the modular integration of modeling components, related adjoint models, and optimization algorithms which makes it well suited for the control of large-scale water systems. Interfaces enable its integration into Sup

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Drewnowski, Jakub. "Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement." Water 11, no. 6 (June 11, 2019): 1218. http://dx.doi.org/10.3390/w11061218.

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In modern and cost-effective Wastewater Treatment Plants (WWTPs), processes such as aeration, chemical feeds and sludge pumping are usually controlled by an operating system integrated with online sensors. The proper verification of these data-driven measurements and the control of different unit operations at the same time has a strong influence on better understanding and accurately optimizing the biochemical processes at WWTP—especially energy-intensive biological parts (e.g., the nitrification zone/aeration system and denitrification zone/internal recirculation). In this study, by integrat

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Deo, Bahma, and Satish Kumar. "Dynamic On-Line Control of Stainless Steel Making in AOD." Advanced Materials Research 794 (September 2013): 50–62. http://dx.doi.org/10.4028/www.scientific.net/amr.794.50.

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A new dynamic control model based on simultaneous mass transfer of C, Cr, and Mn and dynamic heat balance is developed. It allows dynamic adjustment of argon-oxygen ratio. The model is implemented through Level II control system. The total operating period of one heat is divided into five different stages: charge calculation, first blow period, second blow period, third blow period and, lastly, the reduction stage. The charge calculation model, based on heat balance, mass balance and the costs decided the optimum charge mix to start with. Both linear and non-linear regression models are used t

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Al-Radhi, Mohammed Salah Hamza, Safa Jameel Dawood Al-Kamil, and Szakács Tamás. "A model-based machine learning to develop a PLC control system for Rumaila degassing stations." Journal of Petroleum Research and Studies 10, no. 4 (December 21, 2020): 1–18. http://dx.doi.org/10.52716/jprs.v10i4.364.

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Degassing station breakdowns can be dangerous to the operator health and the environment. Programmable logic controllers (PLCs) are key modules of manufacturing control systems that are applied in the complex oil and gas units to reduce manpower and unnecessary faults. However, feeding a PLC with data is a difficult part due to the need of system log files which records all events that occur in the oil fields and provide visibility to a given environment. Moreover, most critical chemical processing plants and oil distributions are visualized and inspected by Supervisory Control and Data Acquis

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He, Lei, Bo Lei, Haiquan Bi, and Tao Yu. "Simplified Building Thermal Model Used for Optimal Control of Radiant Cooling System." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/2976731.

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MPC has the ability to optimize the system operation parameters for energy conservation. Recently, it has been used in HVAC systems for saving energy, but there are very few applications in radiant cooling systems. To implement MPC in buildings with radiant terminals, the predictions of cooling load and thermal environment are indispensable. In this paper, a simplified thermal model is proposed for predicting cooling load and thermal environment in buildings with radiant floor. In this thermal model, the black-box model is introduced to derive the incident solar radiation, while the genetic al

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Betti, Alessandro, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, and Dimitri Thomopulos. "Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps." Sensors 21, no. 5 (March 1, 2021): 1687. http://dx.doi.org/10.3390/s21051687.

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In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in pred

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Luo, Zhikun, Zhifeng Sun, Fengli Ma, Yihan Qin, and Shihao Ma. "Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment." Energies 13, no. 16 (August 12, 2020): 4158. http://dx.doi.org/10.3390/en13164158.

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As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical

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Wang, Xian, Qiancheng Zhao, Xuebing Yang, and Bing Zeng. "Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data." Measurement and Control 53, no. 1-2 (December 2, 2019): 164–80. http://dx.doi.org/10.1177/0020294019888239.

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In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters

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Vahidzadeh, Mohsen, and Corey D. Markfort. "An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions." Energies 13, no. 4 (February 17, 2020): 891. http://dx.doi.org/10.3390/en13040891.

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Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of

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Shao, Yan Chao, Liang Jun Xu, Yan Zhu Hu, and Xin Bo Ai. "Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR." Advanced Materials Research 962-965 (June 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.564.

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Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal component

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Gaska, Krzysztof, and Agnieszka Generowicz. "SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study." Energies 13, no. 13 (June 30, 2020): 3338. http://dx.doi.org/10.3390/en13133338.

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The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management sy

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Liu, Simeng, and Gregor P. Henze. "Evaluation of Reinforcement Learning for Optimal Control of Building Active and Passive Thermal Storage Inventory." Journal of Solar Energy Engineering 129, no. 2 (October 31, 2006): 215–25. http://dx.doi.org/10.1115/1.2710491.

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This paper describes an investigation of machine learning for supervisory control of active and passive thermal storage capacity in buildings. Previous studies show that the utilization of active or passive thermal storage, or both, can yield significant peak cooling load reduction and associated electrical demand and operational cost savings. In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. The reinforcement learning controller learns to operate the building and cooling plant based o

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