Gaussian process model based predictive control

Gaussian process models provide a probabilistic non-parametric modelling approach for black-box identification of non-linear dynamic systems. The Gaussian processes can highlight areas of the input space where prediction quality is poor, due to the lack of data or its complexity, by indicating the higher variance around the predicted mean. Gaussian process models contain noticeably less coefficients to be optimized. This paper illustrates possible application of Gaussian process models within model-based predictive control. The extra information provided within Gaussian process model is used in predictive control, where optimization of control signal takes the variance information into account. The predictive control principle is demonstrated on control of pH process benchmark

Development of Fuzzy Logic pH controller for pH Neutralization in Waste Water Treatment Plant (WWTP)

pH neutralization is one of the important processes in Wastewater Treatment Plant (WWTP). As WWTP used to treat eftluent and discharged water to public sewage, the right pH is important to save the enviromnent, safety, fulfill the rules and regulations and keep the plant equipment in safe condition. pH neutralization is well known fur highly non-linear characteristic with high sensitivity at the neutral region. Thus, pH neutralization is of the hardest parameter to be controlled in WWTP. From the literature review, current and most popular control strategy like PI controller produces certain range of errors. This is due to PI controller unable to compensate the non-linear characteristic of pH neutralization process. Advanced control strategy is believed to be the solution to control pH neutralization process in WWTP. Therefore, the objective of this project is to investigate and design advanced control strategy. A mathematical modeling is used to develop a plant model based on McAvoy Model. The plant model is obtained from previous researcher. From the validation of the mode~ the plant is accepted to be used throughout the project. Both conventional and advanced control strategy is developed. The development of controller is carried out in SIMULINK enviromnent. Evaluation of the controller performance is based on few parameters such as settling time, rise time and integral of absolute error. Based on the simulation results, Fuzzy Logic has given a excellent control performance compared to PI controller. Fuzzy logic is able to compensate any changes in high sensitivity in neutral region. In conclusion, advanced control strategy of Fuzzy Logic able to deal with high nonlinearity of pH neutralization process compared to conventional control strategy which is PI controller. The findings in this project will encourage other researcher to implement it fur other non-linear processes and being implemented in real time industry

Practical modelling and control implementation studies on a pH neutralization process pilot plant

In recent years the industrial application of advanced control techniques for the process industries has become more demanding, mainly due to the increasing complexity of the processes themselves as well as to enhanced requirements in terms of product quality and environmental factors. Therefore the process industries require more reliable, accurate, robust, efficient and flexible control systems for the operation of process plant. In order to fulfil the above requirements there is a continuing need for research on improved forms of control. There is also a need, for a variety of purposes including control system design, for improved process models to represent the types of plant commonly used in industry. Advanced technology has had a significant impact on industrial control engineering. The new trend in terms of advanced control technology is increasingly towards the use of a control approach known as an “intelligent” control strategy. Intelligent control can be described as a control approach or solution that tries to imitate important characteristics of the human way of thinking, especially in terms of decision making processes and uncertainty. It is also a term that is commonly used to describe most forms of control systems that are based on artificial neural networks or fuzzy logic. The first aspect of the research described in the thesis concerns the development of a mathematical model of a specific chemical process, a pH neutralization process. It was intended that this model would then provide an opportunity for the development, implementation, testing and evaluation of an advanced form of controller. It was also intended that this controller should be consistent in form with the generally accepted definition of an “intelligent” controller. The research has been based entirely around a specific pH neutralization process pilot plant installed at the University Teknologi Petronas, in Malaysia. The main feature of interest in this pilot plant is that it was built using instrumentation and actuators that are currently used in the process industries. The dynamic model of the pilot plant has been compared in detail with the results of experiments on the plant itself and the model has been assessed in terms of its suitability for the intended control system design application. The second stage of this research concerns the implementation and testing of advanced forms of controller on the pH neutralization pilot plant. The research was also concerned with the feasibility of using a feedback/feedforward control structure for the pH neutralization process application. Thus the study has utilised this control scheme as a backbone of the overall control structure. The main advantage of this structure is that it provides two important control actions, with the feedback control scheme reacting to unmeasured disturbances and the feedforward control scheme reacting immediately to any measured disturbance and set-point changes. A non-model-based form of controller algorithm involving fuzzy logic has been developed within the context of this combined feedforward and feedback control structure. The fuzzy logic controller with the feedback/feedforward control approach was implemented and a wide range of tests and experiments were carried out successfully on the pilot plant with this type of controller installed. Results from this feedback/feedforward control structure are extremely encouraging and the controlled responses of the plant with the fuzzy logic controller show interesting characteristics. Results obtained from tests of these closed-loop system configurations involving the real pilot plant are broadly similar to results found using computer-based simulation. Due to limitations in terms of access to the pilot plant the investigation of the feedback/feedforward control scheme with other type of controllers such as Proportional plus Integral (PI) controller could not be implemented. However, extensive computer-based simulation work was carried out using the same control scheme with PI controller and the control performances are also encouraging. The emphasis on implementation of advanced forms of control with a feedback/feedforward control scheme and the use of the pilot plant in these investigations are important aspects of the work and it is hoped that the favourable outcome of this research activity may contribute in some way to reducing the gap between theory and practice in the process control field

Fuzzy Logic for pH Neutralization Process

pH neutralization process is a process that is widely studied due to its highly nonlinear process reaction. Its nonlinearity behavior is caused by static nonlinearity between pH and concentration. This nonlinearity depends on the substances in the solution and on their concentrations. In this project, the nonlinearity of the process was investigated. Later, the mathematical model of the process was developed based on McAvoy et al [I]. In addition to the mathematical model, an empirical model was also obtained from Analytical & Chemical Pilot Plant located in the Process Control & Instrumentation Laboratory (23-00-06). Both models were then used to develop the Fuzzy Logic Controller (FLC) by using Advanced-Neuro Fuzzy Inference System (ANFIS) and also gain-scheduling method. In ANFIS implementation for empirical model, the FLC output was identical to the output from PID. Therefore it is concluded that FLC could be used to replace PID for empirical model. In ANFIS implementation for mathematical model, the FLC also could be implemented for mathematical model since the controlled variable successfully follows all the set point changes. For gainscheduling method, the FLC was tested on servo and regulator problems. The servo test was performed by using a random number generator to generate random pH set points between 3 and 11 and the simulation is performed for 100 seconds. The result for the servo test was similar with the result from the ANFIS implementation for mathematical model. For regulator test, the disturbance was the ±20% variation in acid flow. The result for the regulator shows, the controller manages to eliminate the disturbance effect in the process variable. In overall, the project successfully shows that FLC could be a good alternative to PID controller

INVESTIGATION OF ADVANCED CONTROL STRATEGY FOR A pH NEUTRALIZATION PROCESS PLANT

pH neutralization is one of the crucial processes to all industries with various functions range from food processing industry to wastewater treatment. Hence, the process must be maintained at optimum performance to fulfill its functionality. However, pH neutralization is a highly nonlinear process with high sensitivity at the neutralization point. The complexity of the process has challenged the conventional control strategy's performance. Currently, the control strategy used in the pilot plant (PI controller) is adequate with certain range of error. Thus, the objective of this project is to investigate, design and implement advanced control strategy which can improve the overall performance of the conventional control strategy. The calibration results show that the pilot plant's measuring meters have poor accuracy and repeatability. Due to this, no practical experiments have been performed throughout this research. Prior to simulation works, the pilot plant's model obtained from other researcher has been validated. The simulation results show that the model has faster dynamic response compare to the pilot plant. Nevertheless, the model is still being used for simulation. Through this research, the limitation of PI control strategy in controlling nonlinear process has been observed. Fuzzy logic controller (FLC) has been developed to improve the control performance of PI controller. According to the simulation results, FLC has produced excellent control performance with the ability of controlling process' nonlinear region. As a conclusion, advanced control strategy such as FLC is more superior to PI controller in nonlinear process control. For further research, perhaps the advanced control strategy developed can be implemented in the pilot plant to examine its real time performance

On-Line Construction and Rule Base Simplification by Replacement in Fuzzy Systems Applied to a Wastewater Treatment Plant

Evolving Takagi-Sugeno (eTS) fuzzy models are used to build a computational model for the WasteWater Treatment Plant (WWTP) in a paper mill. The fuzzy rule base is constructed on-line from data using a recursive fuzzy clustering algorithm that develops the model structure and parameters. In order to avoid some redundancy in the fuzzy rule base mechanisms for merging membership functions and simplifying fuzzy rules are introduced. The rule base simplification is done by replacement allowing the preservation of the rule (cluster) centres as data points belonging to the original data set. Results for the WWTP show that it is possible to build less complex models and preserve a good balance between accuracy and transparency. Copyright © 2005 IFA

[MODELING OF PH NEUTRALIZATION PROCESS PILOT PLANT]

System Identification is an art of constructing a mathematical model for a dynamic response system. The modeling process is based on the observed input and output data for a system. To start a modeling process, a good understanding of process behavior is required as it will determine the important parameters and characteristics to be analyzed. pH neutralization is a very nonlinear process. It is not easy to get an accurate model as compared to the actual model. Modeling using conventional methods does not seem to give a reliable model for this process. Thus, for wide a range of neutralization pH values, conventional modeling methods are not sufficient. Therefore, for this project, intelligent approaches are considered. The conventional methods that are used by the Author are mathematical modeling, empirical modeling and statistical modeling. Mathematical modeling is done to see the relation of inputs and output. Empirical modeling is the common method used for plant modeling. Statistical modeling is more a to computerized modeling where it requires a good computer configuration basic in order to achieve the desired output. Neural Network is used for the intelligent method. Neural network is an intelligent approach that has the capability to predict future plant performance by training several datasets. These conventional and intelligent methods are compared between each other in term of the model accuracy, model reliability and flexibility. Modeling using mathematical modeling is tedious and requires more effort on the block diagram configuration in order to get an accurate result. Empirical modeling is basically good enough for plant identification, unfortunately for a highly nonlinear system, the method does not seem reliable. Statistical modeling has the ability to predict an acceptable higher order model. On top of that, neural network could give a more reliable and accurate result

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