Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Optimal Control of Unknown Nonlinear System From Inputoutput Data

Optimal control designers usually require a plant model to design a controller. The problem is the controller\u27s performance heavily depends on the accuracy of the plant model. However, in many situations, it is very time-consuming to implement the system identification procedure and an accurate structure of a plant model is very difficult to obtain. On the other hand, neuro-fuzzy models with product inference engine, singleton fuzzifier, center average defuzzifier, and Gaussian membership functions can be easily trained by many well-established learning algorithms based on given input-output data pairs. Therefore, this kind of model is used in the current optimal controller design. Two approaches of designing optimal controllers of unknown nonlinear systems based on neuro-fuzzy models are presented in the thesis. The first approach first utilizes neuro-fuzzy models to approximate the unknown nonlinear systems, and then the feasible-direction algorithm is used to achieve the numerical solution of the Euler-Lagrange equations of the formulated optimal control problem. This algorithm uses the steepest descent to find the search direction and then apply a one-dimensional search routine to find the best step length. Finally several nonlinear optimal control problems are simulated and the results show that the performance of the proposed approach is quite similar to that of optimal control to the system represented by an explicit mathematical model. However, due to the limitation of the feasible-direction algorithm, this method cannot be applied to highly nonlinear and dimensional plants. Therefore, another approach that can overcome these drawbacks is proposed. This method utilizes Takagi-Sugeno (TS) fuzzy models to design the optimal controller. TS fuzzy models are first derived from the direct linearization of the neuro-fuzzy models, which is close to the local linearization of the nonlinear dynamic systems. The operating points are chosen so that the TS fuzzy model is a good approximation of the neuro-fuzzy model. Based on the TS fuzzy model, the optimal control is implemented for a nonlinear two-link flexible robot and a rigid asymmetric spacecraft, thus providing the possibility of implementing the well-established optimal control method on unknown nonlinear dynamic systems

Modelling and control for composition of non-isothermal Continuous Stirred Tank Reactor (CSTR) using fuzzy logic

As a matter of fact, several of chemical or petrochemical industries still using the old technology of conventional control; one of it is PID controller. This is due to the limitation budget of the companies provided. But it stills had a lot of weaknesses that need to be concerned, which it’s the accuracy and it’s precision. Due to that reason, the researchers had found the initiative to solve this situation by creating the Artificial Intelligence (AI), one of it is the Fuzzy Logic. For this research paper, it will introduces the concept of Fuzzy Logic approach towards the control system of non –isothermal continuous stirred tank reactor (CSTR). This simulation study had been made by using the MATLAB SIMULINK, and there will be a comparison with PID controller in order to justify the effectiveness of the modern technology concept in the control system. The result had shown that the fuzzy logic approach can gives the most favorable result in term of its accuracy and robustness. It is clear that this modern approach is better compared with the conventional PID controller

Study on Control of CSTR Using Intelligent Strategies

A non linearity, interaction and complexity in modeling lead to a great difficulties in CSTR control system, this paper interested in CSTR control using PID, fuzzy logic and intelligent control strategies. Water flow rate and ethyl acetate were selected as manipulated variables while sodium acetate and reactor temperature as controlled variables. Firstly the system identification was conducted and the results show that the multi input multi output system can be represented by the following matrix The compare among the strategies show preference of fuzzy control

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Modelling and Control of Chemical Processes using Local Linear Model Networks

Recently, technology and research in control systems have made fast progress in numerous fields, such as chemical process engineering. The modelling and control may face some challenges as the procedures applied to chemical reactors and processes are nonlinear. Therefore, the aim of this research is to overcome these challenges by applying a local linear model networks technique to identify and control temperature, pH, and dissolved oxygen. The reactor studied exhibits a nonlinear function, which contains heating power, flow rate of base, and the flow rate of air as the input parameters and temperature, pH, and dissolved oxygen (pO2) the output parameters. The local linear model networks technique is proposed and applied to identify and control the pH process. This method was selected following a comparison of radial basis function neural networks (RBFNN) and adaptive neuro-fuzzy inference system (ANFIS). The results revealed that local linear model networks yielded less mean square errors than RBFNN and ANFIS. Then proportional-integral (PI) and local linear model controllers are implemented using the direct design method for the pH process. The controllers were designed on the first order pH model with 4 local models and the scaling factor is 20. Moreover, local linear model networks are also used to identify and control the level of dissolved oxygen. To select the best method for system identification, a gradient descent learning algorithm is also used to update the width scaling factor in the network, with findings compared to the manual approach for local linear model networks. However, the results demonstrated that manually updating the scaling factor yielded less mean square error than gradient descent. Consequently, PI and local linear model controllers are designed using the direct design method to control and maintain the dissolved oxygen level. The controllers were designed on first and second order pO2 model with 3 local models and the scaling factor is 20. The results for the first order revealed good control performance. However, the results for second order model lead to ringing poles which caused an unstable output with an oscillation in the input. This problem was solved by zero cancellation in the controller design and these results show good control performance. Finally, the temperature process was identified using local linear model networks and PI and local linear model controllers were designed using the direct design method. From the results, it can be observed that the first order model gives acceptable output responses compared to the higher order model. The control action for the output was behaving much better on the first order model when the number of local models M=4, compared with M=3 and M=5. Furthermore, the results revealed that the mean square error became less when the number of local models M=4 in the controller, compared with having number of local models M=3 and M=5

A new T-S fuzzy model predictive control for nonlinear processes

Abstract: In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS-KRR). The TS-KRR strategy approximates the unknown nonlinear systems by learning the Takagi-Sugeno (TS) fuzzy parameters from the input-output data. Two main steps are required to construct the TS-KRR: the first step is to use a clustering algorithm such as the clustering based Particle Swarm Optimization (PSO) algorithm that separates the input data into clusters and obtains the antecedent TS fuzzy model parameters. In the second step, the consequent TS fuzzy parameters are obtained using a Kernel ridge regression algorithm. Furthermore, the TS based predictive control is created by integrating the TS-KRR into the Generalized Predictive Controller. Next, an adaptive, online, version of TS-KRR is proposed and integrated with the GPC controller resulting an efficient adaptive fuzzy generalized predictive control methodology that can deal with most of the industrial plants and has the ability to deal with disturbances and variations of the model parameters. In the adaptive TS-KRR algorithm, the antecedent parameters are initialized with a simple K-means algorithm and updated using a simple gradient algorithm. Then, the consequent parameters are obtained using the sliding-window Kernel Recursive Least squares (KRLS) algorithm. Finally, two nonlinear systems: A surge tank and Continuous Stirred Tank Reactor (CSTR) systems were used to investigate the performance of the new adaptive TS-KRR GPC controller. Furthermore, the results obtained by the adaptive TS-KRR GPC controller were compared with two other controllers. The numerical results demonstrate the reliability of the proposed adaptive TS-KRR GPC method for discrete-time nonlinear systems

Neuro-controller for regulating coolant flow of CSTR system

Temperature control is very vital for processes that induce very high temperatures. Thus the need for tight control to prevent undesired production, or reactor burst. Process control is somehow shared between the fields of chemical engineering (more focus to process), and control engineering (more focus to control), and a mid point is to be reached by both sides to achieve the plant and production requirements, and also to maintain the system stable. Two control schemes are to be explored, PID control and neuro-control. The first one is widely used in all of industry sectors, especially process control. The later one is still a field of discovery and exploration, as all focus is there now to make intelligent systems the new substitutes for the conventional control systems. The system under the study is the non-isothermal CSTR with irreversible reaction A~B. The control scheme using the coolant flow showed that it is more efficient in heat removal and maintaining the system stable and under control, at the same time achieving the economic goal of highest productivity. Another scheme to be in touch is the hybrid control, where by different schemes are combined to compensate for each other, reaching an optimal control structure. For Further improvement to the system design, more disturbances to be included, and more complex processing units are to be tested using the controller schemes proposed. Experimental work is also an advantage to verilY the simulations of the system and the controllers

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

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers