Predictive control vs. PID control of thermal treatment processes

2011 ◽  
pp. 163-174 ◽  
Author(s):  
M. Voicu ◽  
C. Lazar ◽  
F. Schonberger ◽  
O. Pastravanu ◽  
S. Ifrim
Keyword(s):  
Thermal Treatment ◽  
Predictive Control ◽  
Pid Control ◽  
Treatment Processes

scholarly journals Strengthening Mechanisms of Magnesium-Lithium Based Alloys and Composites

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
C. O. Muga ◽  
Z. W. Zhang
Keyword(s):  
Thermal Treatment ◽  
Dislocation Density ◽  
Thermomechanical Processing ◽  
Precipitation Strengthening ◽  
Strengthening Mechanisms ◽  
Engineering Applications ◽  
The Past ◽  
Treatment Processes ◽  
Solution Strengthening ◽  
Suitable Processing

Mg-Li based alloys are widely applied in various engineering applications. The strength of these alloys is modified and enhanced by different strengthening mechanisms. The strengthening mechanisms of these alloys and their composites have been extensively studied during the past decades. Important mechanisms applied to strengthening the alloys include precipitation strengthening, solution strengthening, grain and subgrain strengthening, and dislocation density strengthening. Precipitation and solution strengthening mechanisms are strongly dependent on composition of the alloys and thermal treatment processes, whereas grain and subgrain and dislocation density strengthening mechanisms majorly depend on thermomechanical processing. In this paper, recent studies on conventional processes for the strengthening of Mg-Li based alloys are summarized as they are critical during the alloys design and processing. Main strengthening mechanisms are objectively reviewed, focusing on their advantages and drawbacks. These can contribute to enhancing, initiating, and improving future researches for alloys design and suitable processing selection.

Energy Efficiency and Tracking Performance Evaluation for Dual-Mode Model Predictive Control of HVAC Systems

2018 ◽  
Vol 10 (4) ◽  
Author(s):  
Zicheng Cai ◽  
Asad A. Ul Haq ◽  
Michael E. Cholette ◽  
Dragan Djurdjanovic
Keyword(s):  
Energy Consumption ◽  
Predictive Control ◽  
Pid Control ◽  
Control Structure ◽  
Tracking Performance ◽  
System Control ◽  
Hvac System ◽  
Dual Mode ◽  
Different Types ◽  
Predictive Controller

This paper presents evaluation of the energy consumption and tracking performance associated with the use of a recently introduced dual-mode model predictive controller (DMMPC) for control of a heating, ventilation, and air conditioning (HVAC) system. The study was conducted using detailed simulations of an HVAC system, which included a multizone air loop, a water loop, and a chiller. Energy consumption and tracking performance are computed from the simulations and evaluated in the presence of different types and magnitudes of noise and disturbances. Performance of the DMMPC is compared with a baseline proportional-integral-derivative (PID) control structure commonly used for HVAC system control, and this comparison showed clear and consistent superiority of the DMMPC.

scholarly journals Performance improvement of wastewater treatment processes by application of machine learning

2020 ◽  
Vol 82 (12) ◽  
pp. 2671-2680
Author(s):  
O. Icke ◽  
D. M. van Es ◽  
M. F. de Koning ◽  
J. J. G. Wuister ◽  
J. Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Wastewater Treatment ◽  
Predictive Control ◽  
Stable Operation ◽  
Load Prediction ◽  
Network Modelling ◽  
Quality Requirements ◽  
Effluent Quality ◽  
Treatment Processes ◽  
Advanced Analytics

Abstract Improving wastewater treatment processes is becoming increasingly important, due to more stringent effluent quality requirements, the need to reduce energy consumption and chemical dosing. This can be achieved by applying artificial intelligence. Machine learning is implemented in two domains: (1) predictive control and (2) advanced analytics. This is currently being piloted at the integrated validation plant of PUB, Singapore's National Water Agency. (1) Primarily, predictive control is applied for optimised nutrient removal. This is obtained by application of a self-learning feedforward algorithm, which uses load prediction and machine learning, fine–tuned with feedback on ammonium effluent. Operational results with predictive control show that the load prediction has an accuracy of ≈88%. It is also shown that an up to ≈15% reduction of aeration amount is achieved compared to conventional control. It is proven that this load prediction-based control leads to stable operation and meeting effluent quality requirements as an autopilot system. (2) Additionally, advanced analytics are being developed for operational support. This is obtained by application of quantile regression neural network modelling for anomaly detection. Preliminary results illustrate the ability to autodetect process and instrument anomalies. These can be used as early warnings to deliver data-driven operational support to process operators.

Low-Temperature Thermal Treatment Processes

2007 ◽  
pp. 299-329 ◽  
Author(s):  
Lawrence K. Wang ◽  
Clint Williford ◽  
Wei-Yin Chen ◽  
Nazih K. Shammas
Keyword(s):  
Thermal Treatment ◽  
Low Temperature ◽  
Treatment Processes

The Modeling and Control of Leaching Rate in Nickel Stir Leaching Process of Sulfuric Acid

2012 ◽  
Vol 485 ◽  
pp. 165-168
Author(s):  
Qiang Li ◽  
Cheng Zhi Yang ◽  
Wen Bo Zhang ◽  
Yang Yu
Keyword(s):  
Computer Simulation ◽  
Sulfuric Acid ◽  
Predictive Control ◽  
Pid Control ◽  
Rate Control ◽  
Leaching Rate ◽  
Modeling And Control ◽  
Leaching Process ◽  
And Control ◽  
Nickel Leaching

Leaching rate is one of the key parameters in the nickel stir leaching process of sulfuric acid and it is hard to online measure directly due to a lot of uncertain facts. In this paper, the prediction model of nickel leaching rate is established by least squares identification method. A controller combining predictive control(PFC) and PID control is designed to control nickel leaching rate in stir leaching process of sulfuric acid and better results of leaching rate control is proved by computer simulation.

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Takao Sato ◽  
Toru Yamamoto ◽  
Nozomu Araki ◽  
Yasuo Konishi
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Design Method ◽  
Design Parameters ◽  
Generalized Predictive Control ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

Investigation of Thermal Treatment Processes for Dissimilar Wafer Bonding

2017 ◽  
Vol 77 (5) ◽  
pp. 143-152
Author(s):  
Chenxi Wang ◽  
Yue Li ◽  
Yannan Liu ◽  
Zhitian Yuan ◽  
Yanhong Tian ◽  
...  
Keyword(s):  
Thermal Treatment ◽  
Wafer Bonding ◽  
Treatment Processes
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