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Abstract: Indirect electrolysis has been used for the removal of chlorophyll a (as indicator of algae) from the final effluent of aerated lagoons in the wastewater treatment plant of Bu-Ali Industrial Estate. The efficiency of the process was studied experimentally and by simulation using neural networks. The process analysis was done in different conditions of retention time (5–50min) and using two types of electrodes based on aluminum and stainless steel, with different distances between electrodes (from 1.0 to 3.5cm). The electrical current and the average voltage applied were between 5 and 90A (0.74–12Adm−3) and 50V, respectively. The influence of the main parameters of the electrolysis process on the final values for chlorophyll a, TSS and COD is evaluated experimentally. On the other hand, predictions of the main system outputs of a treated waste as a function of initial characteristics (initial values of chlorophyll a, TSS, COD) and operation conditions (temperature, electric power, time, electrode distance, and electrode type) were performed using artificial neural networks. The modeling methodologies elaborated in this paper are based on different types of neural networks, used individually or aggregated in stacks. They were developed gradually in the sense of improving the model performance. The neural network results represent accurate predictions, useful for experimental practice. [Copyright &y& Elsevier]

Abstract: This paper discusses a computationally efficient approach to set-point optimisation which cooperates with predictive control and its application to a multivariable neutralisation reactor. In the presented system structure a neural Hammerstein model of the process is used. For set-point optimisation, a linearisation of the steady-state model derived from the neural Hammerstein model is calculated on-line. As a result, the set-point is determined from a linear programming problem. For predictive control, a linear approximation of the neural Hammerstein model is calculated on-line and the control policy is determined from a quadratic programming problem. Thanks to linearisation, the necessity of on-line nonlinear optimisation is eliminated. This article emphasises advantages of neural Hammerstein models: accuracy, a limited number of parameters and a simple structure. Thanks to using such models, model transformations can be carried out very efficiently on-line. It is demonstrated that results obtained in the presented structure are very close to those achieved in a computationally demanding structure with on-line nonlinear optimisation. It is also shown that for the considered neutralisation reactor the classical system structure in which for control and set-point optimisation linear models are used gives numerically wrong results. [Copyright &y& Elsevier]

Abstract: This contribution describes the pattern recognition based data analysis of an existing industrial batch dryer, and the comparison of three artificial intelligence techniques suited to perform classification tasks: neural networks trained using the Levenberg–Marquardt and the Levenberg–Marquardt method with Bayesian regularization, the neuro-fuzzy model based on clustering and grid partition, and the Takagi–Sugeno fuzzy models. The classifiers are used to quantify the dryer batch time and its variation during a certain production period, thus the motivation behind the work is genuine. The presented pattern recognition method implements a supervised learning approach and is based on pressure measurement profiles recorded by the plant data management software—the PI System from OSIsoft. It is found that the neural networks trained with the Bayesian regularization have shown the most robust classification performance with respect to separation threshold selection. Furthermore, it is concluded that the application of artificial intelligence techniques in real chemical manufacturing facilities is feasible and provides useful information for process performance monitoring purposes. The pattern recognition findings presented in this paper are not case specific and can be directly used for the monitoring of a large variety of drying processes since the pressure profile features – vacuum check, pressure decrease, vacuum break – do not depend on the chemicals which are dried. Since the development of the artificial intelligent classifiers is presented in detail and step by step, this work may be interesting as a pattern recognition tutorial for chemical engineers. [Copyright &y& Elsevier]