Experimental Study on Model Predictive Control to Minimize Temperature Change of Vertical Plate With Varying Heat Generation

2011 ◽  
Author(s):  
Shigeki Hirasawa ◽  
Tsuyoshi Kawanami
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Temperature Change ◽  
Predictive Control ◽  
Heat Generation ◽  
Step Response ◽  
Response Patterns ◽  
Object Position ◽  
Experimental Apparatus ◽  
Step Responses

Precise temperature control of 0.001°C under noise-temperature change of 0.1°C is required in semiconductor manufacturing process. We made an experimental apparatus of a vertical steel plate placed in an atmosphere with a varying noise-heat-generation and a control-heater. The noise-heat-generation is that the heating-OFF and ON every 300 s, and it makes temperature change of 3°C at an object position in the plate without control. The control-heater is controlled with the model predictive control method of 5 s interval with two monitoring temperatures to minimize temperature change at the object position in the plate. In this work, we study the effect of the dynamic predictive model on the temperature change at the object position and examine how to make the best dynamic predictive model. Three methods to make the dynamic predictive model are examined: (1) dynamic step responses are obtained by experiment, (2) dynamic step responses are obtained by calculation with a network model of the object, and (3) both step response patterns are combined. When the step response patterns obtained by experiment and calculation are combined to use, the minimum temperature change at the object position is 0.06°C and 1/50 times smaller than that without control. Also, the effect of artificial error in the dynamic predictive model on temperature change at the object position is examined by numerical simulation.

Study on Model Predictive Control to Minimize Temperature Change of Vertical Plate With Varying Heat Generation

2009 ◽  
Author(s):  
Shigeki Hirasawa ◽  
Shinya Ito ◽  
Kazuya Koike
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Predictive Model ◽  
Temperature Change ◽  
Predictive Control ◽  
Temperature Control ◽  
Heat Generation ◽  
Vertical Plate ◽  
Experimental Result ◽  
Object Position

Precise process temperature control of 0.001°C under circumstances of noise-temperature change of 0.1°C is required in semiconductor manufacturing process. We studied optimum control method to minimize temperature change at an object position in a 2-dimensional vertical plate with a varying noise-heat-generation and a control-heater. We numerically calculated 2-dimensional unsteady thermal conduction in the plate with feedback control, feed-forward control, and model predictive control of the control-heat-generation. The temperature change at the object position can be decreased 1/80 times smaller than that without control-heat-generation using the feedback control with two monitoring temperatures. The temperature change at the object position can be decreased 1/1000 times (0.002°C) using the model predictive control of 5 s interval with step response pattern as a dynamic predictive model. We found that the accuracy of the dynamic predictive model is very important for precise temperature control. Experiment was performed for the model predictive control with a network model as the dynamic predictive model, and the experimental result agreed with the calculation result.

scholarly journals Adaptive Nonlinear Model Predictive Control of the Combustion Efficiency under the NOx Emissions and Load Constraints

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1738
Author(s):  
Zhenhao Tang ◽  
Xiaoyan Wu ◽  
Shengxian Cao
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Working Conditions ◽  
Predictive Control ◽  
Combustion Process ◽  
Combustion Efficiency ◽  
Operating Conditions ◽  
Support Vector ◽  
Nox Emissions ◽  
Proposed Model

A data-driven modeling method with feature selection capability is proposed for the combustion process of a station boiler under multi-working conditions to derive a nonlinear optimization model for the boiler combustion efficiency under various working conditions. In this approach, the principal component analysis method is employed to reconstruct new variables as the input of the predictive model, reduce the over-fitting of data and improve modeling accuracy. Then, a k-nearest neighbors algorithm is used to classify the samples to distinguish the data by the different operating conditions. Based on the classified data, a least square support vector machine optimized by the differential evolution algorithm is established. Based on the boiler key parameter model, the proposed model attempts to maximize the combustion efficiency under the boiler load constraints, the nitrogen oxide (NOx) emissions constraints and the boundary constraints. The experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) The model predictive control algorithm can effectively control the boiler combustion efficiency, the average errors of simulation are less than 5%. The proposed model predictive control method can improve the quality of production, reduce energy consumption, and lay the foundation for enterprises to achieve high efficiency and low emission.

Ultra-Supercritical Power Unit Steam Temperature Control Based on Model Predictive Control

2013 ◽  
Vol 291-294 ◽  
pp. 2240-2243
Author(s):  
Guo Liang Wang ◽  
Wei Wu Yan ◽  
Shi He Chen ◽  
Xi Zhang ◽  
Heng Feng Tian ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Temperature Control ◽  
Step Response ◽  
Outlet Temperature ◽  
Temperature Step ◽  
Steam Temperature ◽  
Efficient Operation ◽  
Response Models ◽  
Simulation Results

The steam temperature control is important to Ultra-supercritical (USC) unit for safe and efficient operation under load tracking. In this paper, a model predictive control (MPC) method is introduced for reheated steam temperature control of USC unit. Two inputs (i.e. damper, spray attemperator) are employed to control two outputs (i.e. primary reheater outlet temperature and finish reheater outlet temperature). Step response models of the reheater temperature are achieved using the two inputs by two outputs model. In simulation, the reheated steam temperature can be controlled around the setpoint closely in load tracking. The simulation results show the effectiveness of the proposed methods.

Analytical Study of Thermal Control Method to Minimize Temperature Change of a Plate With Changing Heat Generation

2007 ◽  
Author(s):  
Shigeki Hirasawa ◽  
Shinya Ito
Keyword(s):  
Feedback Control ◽  
Temperature Change ◽  
Temperature Control ◽  
Heat Generation ◽  
Time Step ◽  
Unsteady Temperature ◽  
Feed Forward ◽  
Object Position ◽  
Feed Forward Control ◽  
Monitoring Time

In semiconductor manufacturing process, very accurate temperature control is required. In this work we studied precise temperature control methods for a simple model by numerical simulation. To minimize temperature change at an object position of a vertical plate with varying a noise-heat-generation, we calculated unsteady temperature change of the plate under the effect of a feedback control and a feed-forward control of a control-heater. The temperature change at the object position can be decreased 1/10 times (0.7°C) using the feedback control of 2 s monitoring-time-step with the control-heater placed between the object position and the noise-heat-generation position. The temperature change at the object position can be decreased 1/1000 times (0.001°C) using the feed-forward control of 2 s monitoring-time-step and 5 s forecasting-time with the control-heater placed between the noise-heat-generation position and the object position.

scholarly journals Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4748
Author(s):  
Janne Huotari ◽  
Antti Ritari ◽  
Jari Vepsäläinen ◽  
Kari Tammi
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Predictive Control ◽  
Unit Commitment ◽  
Optimization Methods ◽  
Energy System ◽  
Mixed Integer ◽  
Power Demand ◽  
Demand Prediction ◽  
Probability Estimates

We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Mediterranean. The ship’s energy system is conceptualized to feature a fuel cell and a battery along standard diesel generating sets for the purpose of reducing local emissions near coasts. The developed method is formulated as a model predictive control (MPC) problem, where a novel 2-stage predictive model is used to predict power demand, and a mixed-integer linear programming (MILP) model is used to solve unit commitment according to the prediction. The performance of the methodology is compared to fully optimal control, which was simulated by optimizing unit commitment for entire measured power demand profiles of trips. As a result, it can be stated that the developed methodology achieves close to optimal unit commitment control for the conceptualized energy system. Furthermore, the predictive model is formulated so that it returns probability estimates of future power demand rather than point estimates. This opens up the possibility for using stochastic or robust optimization methods for unit commitment optimization in future studies.

scholarly journals Broad Learning Aided Model Predictive Control With Application to Continuous Stirred Tank Heater

2021 ◽  
Vol 2 ◽  
Author(s):  
Mo Tao ◽  
Tianyi Gao ◽  
Xianling Li ◽  
Kuan Li
Keyword(s):  
Model Predictive Control ◽  
Predictive Model ◽  
Predictive Control ◽  
Learning Algorithm ◽  
Stirred Tank ◽  
Process Data ◽  
Computational Load ◽  
Model Free ◽  
Predictive Controller ◽  
Continuous Stirred Tank

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.

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