MLP and Elman recurrent neural network modelling for the TRMS

This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology

Artificial Neural Network and its Applications in the Energy Sector – An Overview

In order to realize the goal of optimal use of energy sources and cleaner environment at a minimal cost, researchers; field professionals; and industrialists have identified the expediency of harnessing the computational benefits provided by artificial intelligence (AI) techniques. This article provides an overview of AI, chronological blueprints of the emergence of artificial neural networks (ANNs) and some of its applications in the energy sector. This short survey reveals that despite the initial hiccups at the developmental stages of ANNs, ANN has tremendously evolved, is still evolving and have been found to be effective in handling highly complex problems even in the areas of modeling, control, and optimization, to mention a few

Incorporation of the Generalized Tsk Models in Model Predictive Control

The generalized TSK (GTSK) modeling approach is proved to provide accurate model prediction and to alleviate the computational burden. The scope of this study is to incorporate the GTSK models in the nonlinear model predictive control (NMPC) to improve the overall performance and reliability of NMPC. A novel global optimization method, the Leapfrogging technique, is also used to further improve the NMPC's computational efficiency. Another innovation, the "sawtooth" pattern is used as input signal to generate the GTSK model. The experiments and tests are conducted on a nonlinear process simulation system, in which the NMPC control algorithm was embedded. The virtual process in this simulator is fourth-order-plus-dead-time (FOPDT) process with a nonlinear gain and the environmental effect (noise and disturbance). The controlled process is subject to both soft and hard constraints - soft on both the controlled and the auxiliary variable, and hard on both the limits and rate of change of the manipulated variable. The NMPC performance is evaluated via several simulation experiments, which involved constraint handling, interactions and process nonlinearity. The use of a GTSK model and Leapfrogging as an optimizer were demonstrated as effective for nonlinear model predictive control. The nonlinear model is firstly developed by using GTSK approach. The prediction accuracy of the GTSK model was illustrated and quantified by a comparison with SOPDT model. The GTSK model was much better. The performance of GTSK MPC controller is evaluated via seven sets of dynamic control simulation. The controller showed desirable performance for disturbance rejection, set point tracking, constraint handling, and comprehensive environmental effect handling.School of Chemical Engineerin

Energy efficient control and optimisation techniques for distillation processes

PhD ThesisDistillation unit is one of the most energy intensive processes and is among the major CO2 emitter in the chemical and petrochemical industries. In the quest to reduce the energy consumption and hence the environmental implications of unutilised energy, there is a strong motivation for energy saving procedures for conventional columns. Several attempts have been made to redesign and heat integrate distillation column with the aim of reducing the energy consumption of the column. Most of these attempts often involve additional capital costs in implementing. Also a number of works on applying the second law of thermodynamics to distillation column are focused on quantifying the efficiency of the column. This research aims at developing techniques of increasing the energy efficiency of the distillation column with the application of second law using the tools of advanced control and optimisation. Rigorous model from the fundamental equations and data driven models using Artificial neural network (ANN) and numerical methods (PLS, PCR, MLR) of a number of distillation columns are developed. The data for the data driven models are generated from HYSYS simulation. This research presents techniques for selecting energy efficient control structure for distillation processes. Relative gain array (RGA) and relative exergy array (REA ) were used in the selection of appropriate distillation control structures. The viability of the selected control scheme in the steady state is further validated by the dynamic simulation in responses to various process disturbances and operating condition changes. The technique is demonstrated on two binary distillation systems. In addition, presented in this thesis is optimisation procedures based on second law analysis aimed at minimising the inefficiencies of the columns without compromising the qualities of the products. ANN and Bootstrap aggregated neural network (BANN) models of exergy efficiency were developed. BANN enhances model prediction accuracy and also provides model prediction confidence bounds. The objective of the optimisation is to maximise the exergy efficiency of the column. To improve the reliability of the optimisation strategy, a modified objective function incorporating model prediction confidence bounds was presented. Multiobjective optimisation was also explored. Product quality constraints introduce a measure of penalization on the optimisation result to give as close as possible to what obtains in reality. The optimisation strategies developed were applied to binary systems, multicomponents system, and crude distillation system. The crude distillation system was fully explored with emphasis on the preflash unit, atmospheric distillation system (ADU) and vacuum distillation system (VDU). This study shows that BANN models result in greater model accuracy and more robust models. The proposed ii techniques also significantly improve the second law efficiency of the system with an additional economic advantage. The method can aid in the operation and design of energy efficient column.Commonwealth scholarship commissio

Entwicklung und Implementierung eines Verfahrens zur dynamischen Optimierung von Kraftwerksfahrweisen und Anwendung in kommerziellen Simulationsprogrammen

Nicht zuletzt aufgrund der gegenwärtig steigenden Integration regenerativer Energieträger sehen sich konventionelle Stromerzeugungsanlagen hinsichtlich ihrer Flexibilität und Wirtschaftlichkeit zunehmend höheren Anforderungen gegenübergestellt. Neben vielen technischen Detaillösungen ist vor allem eine optimale Prozessführung von hoher Bedeutung, um diesen Anforderungen nachzukommen. Derartige bestmögliche Prozesssteuerungen können unter verschiedensten Beschränkungen und Bewertungsgesichtspunkten durch die Lösung eines Optimalsteuerungsproblems berechnet werden, wofür jedoch ein explizites Systemmodell vorliegen muss. Dieses ist jedoch in vielen praktischen Fällen - weder bei physischen Anlagen, noch bei den meisten Simulationsumgebungen - nicht gegeben. Gegenstand der vorliegenden Arbeit ist es daher, ein Verfahren zu entwickeln, das diese Lücke schließt und somit eine Anwendung der Theorie der optimalen Steuerungen auf Simulationsumgebungen, aber prinzipiell auch physische Systeme erlaubt. Zur Lösung dieser Aufgabe wurde eine Vorgehensstrategie entwickelt, die zunächst das Ermitteln eines mathematischen Ersatzmodells beinhaltet. Da dieses möglichst viele unterschiedliche Systeme nachbilden können soll, wurde auf die universale Struktur der neuronalen Netze zurückgegriffen. Mit diesen als Ersatzmodell ist dann eine numerische Berechnung der optimalen Steuerungen möglich. Im Rahmen dieser Arbeit wurden unterschiedliche Methoden zur Erzeugung eines Ersatzmodells und zur Optimierung ausgearbeitet und miteinander verglichen. Nach der Entwicklung des Gesamtverfahrens wurde dieses anhand dreier Anwendungsbeispiele mit steigender Komplexität getestet. Auf diese Weise konnte beispielsweise für ein reales Gas-und-Dampf-Kraftwerk in Malaysia eine mögliche Beschleunigung eines Warmstarts und die dazugehörige Steuerung identifiziert werden. In einem anderen Testfall wurde das Lastwechselverhalten eines Steinkohleblocks in Deutschland untersucht. Hierbei ergab sich, dass die in der Anlage implementierten Regelungsschaltungen einen nahezu optimalen Lastwechsel ermöglichen. Darüber hinaus wurde der mögliche Zeitvorteil eines Lastwechsels bei höheren zulässigen Änderungen der Frischdampftemperatur diskutiert. Insgesamt lieferte das Gesamtverfahren unter Beachtung einiger wichtiger Bedingungen gute Ergebnisse sowohl im Schritt der Systemidentifikation als auch in dem der Optimierung

Differential recurrent neural network based predictive control.

In this paper an efficient algorithm to train general differential recurrent neural network (DRNN) is developed. The trained network can be directly used in the nonlinear model predictive control (NMPC) context. The neural network is represented in a general nonlinear state-space form and used to predict the future dynamic behavior of the nonlinear process in real time. In the new training algorithms, the ODEs of the model and the dynamic sensitivity are solved simultaneously using Taylor series expansion and automatic differentiation (AD) techniques. The same approach is also used to solve the online optimization problem in the predictive controller. The efficiency and effectiveness of the DRNN training algorithm and the NMPC approach are demonstrated through a two-CSTR case study. A good model fitting for the nonlinear plant at different sampling rates is obtained using the new method. A comparison with other approaches shows that the new algorithm can considerably reduce network training time and improve solution accuracy. The DRNN based NMPC approach results in good control performance under different operating conditions