International Journal of Scientific Engineering and Science Volume 2, Issue 2, pp. 26-30, 2018. ISSN (Online): 2456-7361 26 http://ijses.com/ All rights reserved Advanced pH Control Using Fuzzy Logic Nasser Mohamed Ramli 1 , Isa Zakaria Muhammad 1 1 Chemical Engineering Department, Faculty of Engineering, Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia-32610 Abstract— Advanced pH control is performed by using fuzzy logic. This paper discuss on achieving the set of objectives for the specific study. These includes using one model system developed for application of process control and to develop steady state model to generate data for synthesizing the basic process control strategy. The next study will be to develop feedback, feedforward, cascade, smith predictor and IMC control strategy in simulation environment. This paper proposes to study the basic control principles, tuning methods, and the pH control. The main software that is used in achieving the aim of the research is to use Simulink in MATLAB environment. Keywords— Dynamic state; fuzzy Logic; pH control. I. INTRODUCTION Nowadays, the advanced control techniques of industrial application become more demanding for process industries. This is due to the increasing complexity of the process and to produce better requirements in terms of product quality and environmental issues. Thus, a stable, efficient and flexible control system is required in continuing the operation of the process. There is also a need, for a variety of purposes including control system design, for improved process model to represent the types of plant commonly used in industry. Advanced technology has major impact on industrial control engineering. There is a new method of advanced control technology that is increasing towards the use of a control approach known as “intelligent” control strategy. Intelligent control act as a control approach or solution that tries to imitate important characteristics of the human way of thinking, basically more on 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. Usually a control theory can be successfully applied only when the system under control can be sufficiently analyzed and a useful mathematical model are used. When the process characteristics are known in advance, and are either constant or change predictably, a non-adaptive controller can be used to control it. Difficulties arise in the control of the pH process due to the severe process non-linearity and frequent load changes [1]. For example, changes in the influent composition or flow rate. The non-linearity can be understood from the s-shaped titration curve. Frequent and rapid load changes are common for most waste water treatment facilities since the influents come from the waste of a number of sources. Therefore, it is difficult to analyze and derive the system model of a pH control process. The theory of fuzzy sets and algorithms developed by Zadeh [2] can be used to evaluated these imprecise linguistic statements directly. Fuzzy logic provides an effective means of capturing the approximate nature of the physical world. Therefore, it can be used to provide an algorithm which can convert a linguistic control strategy based on expert knowledge, into an automatic control strategy [3]. The objectives are to be referred as a guideline to achieve the specific goal in current research. The general objectives are to understand the skills and knowledge about the research. Besides, to apply the knowledge gain and also to adapt with individual independent throughout the research. As for the research, the specific objectives are to test different control strategies on the pH and to provide the best result and to develop fuzzy logic compared with the best control strategies in order to gain optimum result. The research will consists of two main parts; the first is to develop five different control strategies with the pH changes and get the best result. The control strategies involved are feedforward control, feedback control, cascade control, smith predictor control and integral model control (IMC). Second, choose the best control strategies and compared with the fuzzy logic on pH control. The scope of research will be analyzing the types of advanced control strategies available. This will include advantages and disadvantages for the specific control strategies on pH control. II. METHODOLOGY Generally, the development of the fuzzy logic and control schemes involves three steps as shown in figure 1. The first step is the fuzzification process. This process involves a domain transformation in which the system inputs or crisp inputs are converted into fuzzy set inputs. In the pH neutralization process the system inputs are actually the measured process variables such as the pH value in the tank, the flowrates of the streams and the conductivity values of the solutions. In this process each input will be transformed into its own group of membership functions or fuzzy sets. Thus the development of the controller must include the important system inputs, determining the type of membership function, as well as establishing the degree of the membership function for the input set. The second step is the Fuzzy Inference process which is described as a process that forms the mapping of the fuzzy input and output sets. The main process involves establishing the relevant Fuzzy Set and Fuzzy Operator, as well as developing a set of “if-then rule statements”. The last process prior to the next step is the aggregation process in which all the results of implication of each rule are combined into a single fuzzy set [4].