Design and Implementation of an Intelligent PI Controller for a Real Time Non Linear pH Neutralization Process

In many chemical processes, pH is one of the most important parameter and control of the pH is highly non linear due to the complex nature of processes. PID controllers are widely used in process industries to control linear, non-linear and stable, unstable systems. Selection of the suitable controller tuning procedure is important to improve the performance of the PID controller and hence the process variable can be controlled in better manner. In this work, Firefly Algorithm (FA) based intelligent PI controller is attempted for a Non Linear pH control process in real time. The effectiveness of the FA controller is studied in the selected operating regions and the results are validated with Relay Feedback (RFB) method and Particle Swarm Optimization (PSO) method based controllers in the simulation environment. The simulation results indicated that the steady state performance and error performance indices of the FA controller are better than the RFB and PSO controller in the selected operating regions. The FA controller is also implemented in the real time laboratory pH control system, the results confirm that the servo response and regulatory response of the proposed intelligent controller provides better performance with the FA based PI Controllers

Implementation of Robust Virtual Feedback Model Predictive Controller for Chemical Reactor

Abstract: Model Predictive Control (MPC) schemes are now widely used in process industries for the control of key unit operations. In this paper, a state estimation based model predictive controller for nonlinear process has been proposed. The model predictive controller is designed by considering a state space model and an extended Kalman filter to predict the future behaviour of the system. The efficacy of the proposed MPC scheme has been demonstrated by conducting experimentalstudies on a continuous stirred tank reactor, a SISO system.The analysis of the extensive dynamic closed loop studies revealed that, the MPC scheme formulated produces satisfactory performance for servo operation

NON-LINEAR MODEL PREDICTIVE CONTROL STRATEGIES FOR PROCESS PLANTS USING SOFT COMPUTING APPROACHES

The developments of advanced non-linear control strategies have attracted a considerable research interests over the past decades especially in process control. Rather than an absolute reliance on mathematical models of process plants which often brings discrepancies especially owing to design errors and equipment degradation, non-linear models are however required because they provide improved prediction capabilities but they are very difficult to derive. In addition, the derivation of the global optimal solution gets more difficult especially when multivariable and non-linear systems are involved. Hence, this research investigates soft computing techniques for the implementation of a novel real time constrained non-linear model predictive controller (NMPC). The time-frequency localisation characteristics of wavelet neural network (WNN) were utilised for the non-linear models design using system identification approach from experimental data and improve upon the conventional artificial neural network (ANN) which is prone to low convergence rate and the difficulties in locating the global minimum point during training process. Salient features of particle swarm optimisation and a genetic algorithm (GA) were combined to optimise the network weights. Real time optimisation occurring at every sampling instant is achieved using a GA to deliver results both in simulations and real time implementation on coupled tank systems with further extension to a complex quadruple tank process in simulations. The results show the superiority of the novel WNN-NMPC approach in terms of the average controller energy and mean squared error over the conventional ANN-NMPC strategies and PID control strategy for both SISO and MIMO systemsPetroleum Training Development Fun

Linear and Adaptive Controller Designs from Plant Data

Ph.DDOCTOR OF PHILOSOPH

A Model-Based Framework for the Smart Manufacturing of Polymers

It is hard to point a daily activity in which polymeric materials or plastics are not involved. The synthesis of polymers occurs by reacting small molecules together to form, under certain conditions, long molecules. In polymer synthesis, it is mandatory to assure uniformity between batches, high-quality of end-products, efficiency, minimum environmental impact, and safety. It remains as a major challenge the establishment of operational conditions capable of achieving all objectives together. In this dissertation, different model-centric strategies are combined, assessed, and tested for two polymerization systems. The first system is the synthesis of polyacrylamide in aqueous solution using potassium persulfate as initiator in a semi-batch reactor. In this system, the proposed framework integrates nonlinear modelling, dynamic optimization, advanced control, and nonlinear state estimation. The objectives include the achievement of desired polymer characteristics through feedback control and a complete motoring during the reaction. The estimated properties are close to experimental values, and there is a visible noise reduction. A 42% improvement of set point accomplishment in average is observed when comparing feedback control combined with a hybrid discrete-time extended Kalman filter (h-DEKF) and feedback control only. The 4-state geometric observer (GO) with passive structure, another state estimation strategy, shows the best performance. Besides achieving smooth signal processing, the observer improves 52% the estimation of the final molecular weight distribution when compared with the h-DEKF. The second system corresponds to the copolymerization of ethylene with 1,9-decadiene using a metallocene catalyst in a semi-batch reactor. The evaluated operating conditions consider different diene concentrations and reaction temperatures. Initially, the nonlinear model is validated followed by a global sensitivity analysis, which permits the selection of the important parameters. Afterwards, the most important kinetic parameters are estimated online using an extended Kalman filter (EKF), a variation of the GO that uses a preconditioner, and a data-driven strategy referred as the retrospective cost model refinement (RCMR) algorithm. The first two strategies improve the measured signal, but fail to predict other properties. The RCMR algorithm demonstrates an adequate estimation of the unknown parameters, and the estimates converge close to theoretical values without requiring prior knowledge

Improved gravitational search algorithm for proportional integral derivative controller tuning in process control system

Proportional-Integral-Derivative (PID) controller is one of the most used controllers in the industry due to the reliability and simplicity of its structure. However, despite its simple structure controller, the tuning process of PID controller for nonlinear, high-order and complex plant is difficult and faces lots of challenges. Conventional method such as Ziegler-Nichols are still being used for PID tuning process despite its lack of tuning accuracy. Nowadays researchers around the world shift their attention from conventional method to optimisation-based methods. For the last five years, optimisation techniques become one of the most popular methods used for tuning process of PID controller. Optimisation techniques such as Genetic Algorithm (GA), Particle Swarm Optimisation (PSO) as well as Gravitational Search Algorithm (GSA) are widely used for the PID controller application. Despite the effectiveness of GSA for PID controller tuning process compared to the GA and PSO technique, there is still a room for improvement of GSA performance for PID controller tuning process. This research represents the additional characters in GSA to enhance the PID controller parameter tuning performance which are Linear Weight Summation (LWS) and alpha parameter range tuning. Performance of optimisation-based PID controllers are measured based on the transient response performance specification (i.e. rise time, settling time, and percentage overshoot). By implementing these two approaches, results show that Improved Gravitational Search Algorithm (IGSA) based PID controller produced 20% to 30% faster rise and settling time and 25% to 35% smaller percentage overshoot compared to GA-PID and PSO-PID. For real implementation analysis, IGSA based PID controller also produced faster settling time and lower percentage overshoot than other optimisation-based PID controller. A good controller viewed as a controller that produced a stable dynamic system. Therefore, by producing a good transient response, IGSA based PID controller is able to provide a stable dynamic system performance compared to other controllers

Remote maintenance of real time controller software over the internet

The aim of the work reported in this thesis is to investigate how to establish a standard platform for remote maintenance of controller software, which provides remote monitoring, remote fault identification and remote performance recovery services for geographically distributed controller software over the Internet. A Linear Quadratic Gaussian (LQG) controller is used as the benchmark for the control performance assessment; the LQG benchmark variances are estimated based on the Lyapunov equation and subspace matrices. The LQG controller is also utilized as the reference model of the actual controller to detect the controller failures. Discrepancies between control signals of the LQG and the actual controller are employed to a General Likelihood Ratio (GLR) test and the controller failure detection is characterized to detect sudden jumping points in the mean or variance of the discrepancies. To restore the degraded control performance caused by the controller failures, a compensator is designed and inserted into the post-fault control loop, which serially links with the faulty controller and recovers the degraded control performance into an acceptable range. Techniques of controller performance monitoring, controller failure detection and maintenance are extended into the Internet environment. An Internet-based maintenance system for controller software is developed, which provides remote control performance assessment and recovery services, and remote fault identification service over the Internet for the geographically distributed controller software. The integration between the mobile agent technology and the controller software maintenance is investigated. A mobile agent based controller software maintenance system is established; the mobile agent structure is designed to be flexible and the travelling agents can be remotely updated over the Internet. Also, the issue of heavy data process and transfer over the Internet is probed and a novel data process and transfer scheme is introduced. All the proposed techniques are tested on sirnulations or a process control unit. Simulation and experimental results illustrate the effectiveness of the proposed techniques.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Instrumentation and control of anaerobic digestion processes: a review and some research challenges

The final publication is available at Springer via http://dx.doi.org/10.1007/s11157-015-9382-6[EN] To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid mainly industrial then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables measured online to the prediction of overall process perfor- mance based on global off-line measurements to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates)andtheirbioaccessibility andbiodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.The authors acknowledge the financial support of INRA (the French National Institute for Agricultural Research), the French National Research Agency (ANR) for the "Phycover" project (project ANR-14-CE04-0011) and ADEME for Inter-laboratory assay financial support.Jimenez, J.; Latrille, E.; Harmand, J.; Robles Martínez, Á.; Ferrer Polo, J.; Gaida, D.; Wolf, C.... (2015). Instrumentation and control of anaerobic digestion processes: a review and some research challenges. Reviews in Environmental Science and Biotechnology. 14(4):615-648. doi:10.1007/s11157-015-9382-6S615648144Aceves-Lara CA, Latrille E, Steyer JP (2010) Optimal control of hydrogen production in a continuous anaerobic fermentation bioreactor. 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Novel strategies for process control based on hybrid semi-parametric mathematical systems

Tese de doutoramento. Engenharia Química. Universidade do Porto. Faculdade de Engenharia. 201