Mashitah Che Razali

Two-time scales matrix decomposition for wastewater treatment plant

The purpose of this work is to identify two-time scales matrix decomposition of wastewater treatment plant. Wastewater plant is naturally aim to remove suspended substances, organic material and phosphate before releasing to recipients. Initially, the MIMO system is excited with generalized binary noise signals and estimated with robust numerical subspace state-space system identification. The performance of identified models is then validated by variance accounted for. Next, the block diagonalisation procedures are implemented in decoupling the system with two-time property into slow and fast subsystem. It was observed that the identified model possess two-time scales behaviors presented by separated clusters of eigenvalues. Besides, similar dynamic responses were obtained by decoupled systems compared to actual full model at lower frequency that is highly demanded in control design application. The study leads to future improvement on wastewater control strategies.

Singularly Perturbation Method Applied To Multivariable PID Controller Design

Proportional integral derivative (PID) controllers are commonly used in process industries due to their simple structure and high reliability. Efficient tuning is one of the relevant issues of PID controller type. The tuning process always becomes a challenging matter especially for multivariable system and to obtain the best control tuning for different time scales system. This motivates the use of singularly perturbation method into the multivariable PID (MPID) controller designs. In this work, wastewater treatment plant and Newell and Lee evaporator were considered as system case studies. Four MPID control strategies, Davison, Penttinen-Koivo, Maciejowski, and Combined methods, were applied into the systems. The singularly perturbation method based on Naidu and Jian Niu algorithms was applied into MPID control design. It was found that the singularly perturbed system obtained by Naidu method was able to maintain the system characteristic and hence was applied into the design of MPID controllers. The closed loop performance and process interactions were analyzed. It is observed that less computation time is required for singularly perturbed MPID controller compared to the conventional MPID controller. The closed loop performance shows good transient responses, low steady state error, and less process interaction when using singularly perturbed MPID controller.

Application of adaptive decentralized PI controller for activated sludge process

This paper presents the application of adaptive decentralized PI controller to nonlinear activated sludge wastewater treatment plant (WWTP). Tuning of WWTP is a challenging task due to the variation and the high uncertainty of the parameters. Thus, a simple tuning method is applied in satisfying straighten effluent quality and hence optimizing the nitrogen removal of the plant. The PI controller parameters are adjusted directly by updating algorithm developed based on adaptive interaction algorithm theory. It was observed that the decentralized PI with approximate Frechet tuning algorithm offers an attractive tuning task for multivariable WWTP with improvement in energy saving and effluent violations of Benchmark Simulation Model No. 1.

Multivariable PID controllers for dynamic process

This paper is concerned with the design of a dynamic multivariable PID control for multi input multi output (MIMO) process. Four multivariable PID control schemes using Davison, Penttinen-Koivo, Maciejowski and a combined method were applied. The controller parameters for all control strategies were designed based on dynamic condition using singularly perturbed system. The purpose of the study is to investigate the effectiveness in the performance of dynamic control based on different multivariable PID control strategies. To attain the best result, numerous tuning parameters were tested. The simulation results show the significance of the study whereby the proposed dynamic MPID control scheme shows better improvement in control tuning of nonlinear system.

Comparative study of static vs dynamic control for multivariable PID

This paper addresses two different control strategies for multivariable system. The analyzed control strategies are based on Davison and Penttinen-Koivo method. The aims of the study are to investigate the differences in the performance of static and dynamic control for wastewater treatment plant. The method proposed by Davison and Penttinen- Koivo was modified to measure the dynamic characteristic of the wastewater treatment plant. The simulations are tested using Matlab/ Simulink with several tuning parameters. Finally, the simulation graph is provided to illustrate the efficiency of the proposed approach. It was found that the presence of dynamic control is able to keep the output concentration at the desired level with the changes of input concentration, unlike static control which only able to control system with static inputs.

Neuro-fuzzy modelling of wastewater treatment system

Wastewater treatment system is highly uncertain and intricate system. Suitable model is a key to smooth and optimal operation of the system. The available wastewater treatment system models are too difficult to use and costly to experiment. This paper presents neuro-fuzzy modelling of wastewater treatment system. Adaptability, smoothness, effectiveness, reliability, less computational and empirical experimentation costs are some of the advantages of neuro-fuzzy approach. Simulation studies show that the proposed neuro-fuzzy technique yielded outstanding results. Thus, proven the technique is an efficient and valuable tool for modelling wastewater treatment system.

A self-tuning decentralized PI control for multivariable plant

Tuning of controller is one of the most basic functions in control engineering in obtaining optimal control parameters and hence achieving optimal dynamic responses of the system. The work presents the self-tuning PI controller that potential to auto-tune the controller parameters and adapting the changes in controlled process parameter for a MIMO system. The decentralized PI controller with an adaptive interaction algorithm is developed and tested to nonlinear multivariable wastewater treatment plant. It is aims to self-tune the PI control parameters in regulating the substrate and DO concentrations from a steady state operating point. It was observed that the proposed control structure and the tuning algorithm were successfully tracks the desired set-point with considerable effects in noisy environment.