Development of a Data-Driven Soft Sensor for Multivariate Chemical Processes Using Concordance Correlation Coefficient Subsets Integrated with Parallel Inverse-Free Extreme Learning Machine

Nonlinearity, complexity, and technological limitations are causes of troublesome measurements in multivariate chemical processes. In order to deal with these problems, a soft sensor based on concordance correlation coefficient subsets integrated with parallel inverse-free extreme learning machine (CCCS-PIFELM) is proposed for multivariate chemical processes. In comparison to the forward propagation architecture of neural network with a single hidden layer, i.e., a traditional extreme learning machine (ELM), the CCCS-PIFELM approach has two notable points. Firstly, there are two subsets obtained through the concordance correlation coefficient (CCC) values between input and output variables. Hence, impacts of input variables on output variables can be assessed. Secondly, an inverse-free algorithm is used to reduce the computational load. In the evaluation of the prediction performance, the Tennessee Eastman (TE) benchmark process is employed as a case study to develop the CCCS-PIFELM approach for predicting product compositions. According to the simulation results, the proposed CCCS-PIFELM approach can obtain higher prediction accuracy compared to traditional approaches

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

Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering

This paper investigates the use of extended Kalman filtering to train recurrent neural networks with rather general convex loss functions and regularization terms on the network parameters, including $\ell_1$-regularization. We show that the learning method is competitive with respect to stochastic gradient descent in a nonlinear system identification benchmark and in training a linear system with binary outputs. We also explore the use of the algorithm in data-driven nonlinear model predictive control and its relation with disturbance models for offset-free closed-loop tracking.Comment: 21 pages, 3 figures, submitted for publicatio

combining First Principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no 101000733 (PROMICON).Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with First Principles for bioprocess modeling. Here we revisit the general bioreactor hybrid model and introduce some deep learning techniques. Multi-layer networks with varying depths were combined with First Principles equations in the form of deep hybrid models. Deep learning techniques, namely the adaptive moment estimation method (ADAM), stochastic regularization and depth-dependent weights initialization were evaluated in a hybrid modeling context. Modified sensitivity equations are proposed for the computation of gradients in order to reduce CPU time for the training of deep hybrid models. The methods are illustrated with applications to a synthetic dataset and a pilot 50 L MUT+ Pichia pastoris process expressing a single chain antibody fragment. All in all, the results point to a systematic generalization improvement of deep hybrid models over its shallow counterpart. Moreover, the CPU cost to train the deep hybrid models is shown to be lower than for the shallow counterpart. In the pilot 50L MUT+ Pichia pastoris data set, the prediction accuracy was increased by 18.4% and the CPU decreased by 43.4%.publishersversionpublishe

Neural Model-Based Advanced Control of Chylla-Haase Reactor

The objective of this thesis is to develop advanced control method and to design advanced control system for the polymerization reactor (Chylla-Haase) to maintain the high accurate reactor temperature. The first stage of this research start with the development of mathematical model of the process. The sub-models for monomer concentration, polymerization rate, reactor temperature and jacket outlet/inlet temperature are developed and implemented in Matlab/Simulink. Four conventional control methods were applied to the reactor: a Proportional –Integral-Derivative (PID), Cascade control (CCs), Linear-Quadratic-Regulator (LQR), and Linear model predictive control (LMPC). The simulation results show that the PID controller is unable to perform satisfactorily due to the change of physical properties unless constant re-tuning takes place. Also, Cascade Control the most common control method used in such processes cannot guarantee a robust performance under varying disturbance and system uncertainty. In addition, LQR and linear MPC methods lead to better results compared with the previous two methods. But it is still under an assumption of the linearized plant. Three advanced neural network based control schemes are also proposed in this thesis: radial basis function RBF neural network inverse model based feedforward-feedback control scheme, RBF based model predictive control and multi-layer perception (MLP) based model predictive control. The major objective of these control schemes is to maintain the reactor temperature within its tolerance range under disturbances and system uncertainty. Satisfactory control performance in terms of effective regulation and robustness to disturbance have been achieved. In the feedforward-feedback control scheme, a neural network model is used to predict reactor temperature. Then, a neural network inverse model is used to estimate the valve position of the reactor, the manipulated variable. This method can identify the controlled system with the RBF neural network identifier. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance. These advanced methods achieved the much improved control performance compared with conventional control schemes. The main contribution of this research lies in the following aspects. The MPC theory is realised to control Chylla-Haase polymerization reactor. Two adaptive reactor models including the RBF network model and MLP model are developed to predict the multiple-step-ahead values of the reactor output. Their modelling ability is compared with that of the models with fixed parameters and proven to be better. The RBF neural network and the MLP is trained by the recursive Least Squares (RLS) algorithm and is used to model parameter uncertainty in nonlinear dynamics of the Chylla-Haase reactor. The predictive control strategy based on the RBF neural network is applied to achieve set-point tracking of the reactor output against disturbances. The result shows that the RBF based model predictive control gives reliable result in the presence of some disturbances and keeps the reactor temperature within a tight tolerance range around the specified reaction temperature. Moreover, RBF neural network based model predictive control strategy has also been used to reduce the batch time in order to shorten the reaction period. RBF neural network is considered as a prediction model for control purpose which is based to minimize a cost function in order to determine an optimal sequence of control moves. The result shows that the RBF based model predictive control gives reliable result in the presence of variation of monomer and presence of some disturbances for keeping the reactor temperature within a tight tolerance range around the specified reaction temperature without harming the quality of the temperature control

Neural Networks: Training and Application to Nonlinear System Identification and Control

This dissertation investigates training neural networks for system identification and classification. The research contains two main contributions as follow:1. Reducing number of hidden layer nodes using a feedforward componentThis research reduces the number of hidden layer nodes and training time of neural networks to make them more suited to online identification and control applications by adding a parallel feedforward component. Implementing the feedforward component with a wavelet neural network and an echo state network provides good models for nonlinear systems.The wavelet neural network with feedforward component along with model predictive controller can reliably identify and control a seismically isolated structure during earthquake. The network model provides the predictions for model predictive control. Simulations of a 5-story seismically isolated structure with conventional lead-rubber bearings showed significant reductions of all response amplitudes for both near-field (pulse) and far-field ground motions, including reduced deformations along with corresponding reduction in acceleration response. The controller effectively regulated the apparent stiffness at the isolation level. The approach is also applied to the online identification and control of an unmanned vehicle. Lyapunov theory is used to prove the stability of the wavelet neural network and the model predictive controller. 2. Training neural networks using trajectory based optimization approachesTraining neural networks is a nonlinear non-convex optimization problem to determine the weights of the neural network. Traditional training algorithms can be inefficient and can get trapped in local minima. Two global optimization approaches are adapted to train neural networks and avoid the local minima problem. Lyapunov theory is used to prove the stability of the proposed methodology and its convergence in the presence of measurement errors. The first approach transforms the constraint satisfaction problem into unconstrained optimization. The constraints define a quotient gradient system (QGS) whose stable equilibrium points are local minima of the unconstrained optimization. The QGS is integrated to determine local minima and the local minimum with the best generalization performance is chosen as the optimal solution. The second approach uses the QGS together with a projected gradient system (PGS). The PGS is a nonlinear dynamical system, defined based on the optimization problem that searches the components of the feasible region for solutions. Lyapunov theory is used to prove the stability of PGS and QGS and their stability under presence of measurement noise

Data-based PID controller designs for nonlinear systems

Master'sMASTER OF ENGINEERIN

Monitoring and Fault Diagnosis for Chylla-Haase Polymerization Reactor

The main objective of this research is to develop a fault detection and isolation (FDI) methodologies for Cylla-Haase polymerization reactor, and implement the developed methods to the nonlinear simulation model of the proposed reactor to evaluate the effectiveness of FDI methods. The first part of this research focus of this chapter is to understand the nonlinear dynamic behaviour of the Chylla-Haase polymerization reactor. In this part, the mathematical model of the proposed reactor is described. The Simulink model of the proposed reactor is set up using Simulink/MATLAB. The design of Simulink model is developed based on a set of ordinary differential equations that describe the dynamic behaviour of the proposed polymerization reactor. An independent radial basis function neural networks (RBFNN) are developed and employed here for an on-line diagnosis of actuator and sensor faults. In this research, a robust fault detection and isolation (FDI) scheme is developed for open-loop exothermic semi-batch polymerization reactor described by Chylla-Haase. The independent (RBFNN) is employed here when the system is subjected to system uncertainties and disturbances. Two different techniques to employ RBF neural networks are investigated. Firstly, an independent neural network is used to model the reactor dynamics and generate residuals. Secondly, an additional RBF neural network is developed as a classifier to isolate faults from the generated residuals. In the third part of this research, a robust fault detection and isolation (FDI) scheme is developed to monitor the Chylla-Haase polymerization reactor, when it is under the cascade PI control. This part is really challenging task as the controller output cannot be designed when the reactor is under closed-loop control, and the control action will correct small changes of the states caused by faults. The proposed FDI strategy employed a radial basis function neural network (RBFNN) in an independent mode to model the process dynamics, and using the weighted sum-squared prediction error as the residual. The Recursive Orthogonal Least Squares algorithm (ROLS) is employed to train the model to overcome the training difficulty of the independent mode of the network. Then, another RBFNN is used as a fault classifier to isolate faults from different features involved in the residual vector. In this research, an independent MLP neural network is implemented here to generate residuals for detection task. And another RBF is applied for isolation task performing as a classifier. The fault diagnosis scheme is developed for a Chylla-Haase reactor under open-loop and closed-loop control system. The comparison between these two neural network architectures (MPL and RBF) are shown that RBF configuration trained by (RLS) algorithm have several advantages. The first one is greater efficiency in finding optimal weights for field strength prediction in complex dynamic systems. The RBF configuration is less complex network that results in faster convergence. The training algorithms (RLs and ROLS) that used for training RBFNN in chapter (4) and (5) have proven to be efficient, which results in significant faster computer time in comparison to back-propagation one. Another fault diagnosis (FD) scheme is developed in this research for an exothermic semi-batch polymerization reactor. The scheme includes two parts: the first part is to generate residual using an extended Kalman filter (EKF), and the second part is the decision making to report fault using a standardized hypothesis of statistical tests. The FD simulation results are presented to demonstrate the effectiveness of the proposed method. In the lase section of this research, a robust fault diagnosis scheme for abrupt and incipient faults in nonlinear dynamic system. A general framework is developed for model-based fault detection and diagnosis using on-line approximators and adaptation/learning schemes. In this framework, neural network models constitute an important class of on-line approximators. The changes in the system dynamics due to fault are modelled as nonlinear functions of the state, while the time profile of the fault is assumed to be exponentially developing. The changes in the system dynamics are monitored by an on-line approximation model, which is used for detecting the failures. A systematic procedure for constructing nonlinear estimation algorithm is developed, and a stable learning scheme is derived using Lyapunov theory. Simulation studies are used to illustrate the results and to show the effectiveness of the fault diagnosis methodology. Finally, the success of the proposed fault diagnosis methods illustrates the potential of the application of an independent RBFNN, an independent MLP, an Extended kalman filter and an adaptive nonlinear observer based FD, to chemical reactors

Control and soft sensing strategies for a wastewater treatment plant using a neuro-genetic approach.

During the last years, machine learning-based control and optimization systems are playing an important role in the operation of wastewater treatment plants in terms of reduced operational costs and improved effluent quality. In this paper, a machine learning-based control strategy is proposed for optimizing both the consumption and the number of regulation violations of a biological wastewater treatment plant. The methodology proposed in this study uses neural networks as a soft-sensor for on-line prediction of the effluent quality and as an identification model of the plant dynamics, all under a neuro-genetic optimum model-based control approach. The complete scheme was tested on a simulation model of the activated sludge process of a large-scale municipal wastewater treatment plant running under the GPS-X simulation frame and validated with operational gathered data, showing optimal control performance by minimizing operational costs while satisfying the effluent requirements, thus reducing the investment in expensive sensor devices