A control system based on a combination of two artificial neural networks (ANN's) was designed for the pH-neutralization of acidic liquid streams. The first ANN is a plant neural model that predicts future pH values from past/present values of pH and valve stem position and future values of valve stem position. The second ANN is a plant inverse neural model that calculates the future valve stem positions from present/past values of pH and valve stem position and future values of set point. The performance of the controller was studied first by numerical simulation. The controller was further implemented in a continuous stirred tank reactor in which the neutralization of acetic and propionic acids with sodium hydroxide was performed. The controller robustness and adaptive performance were tested under different perturbations of flow, composition, and set point and several buffering changes.

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The byproduct so-called alperujo (a solid-liquid byproduct generated from olive oil extraction by two-phase centrifugation) is difficult to dry due to its high moisture content. An artificial neural network (ANN) was developed to model the drying of alperujo in fluidized bed dryers. The ANN, a three layer perceptron (3 inputs, 4 hidden nodes, and 1 output) with back-propagation updating, serves to predict the moisture of the output solid at a time t + T from known input data at time t. The input data are the actual values of the input air temperature, fluidized bed temperature, and output solid moisture. T is the sampling time. The ANN learning, topology optimization, and verification are described in this paper. The essential data to design the ANN were taken from runs in a bench-scale dryer, and the ANN validation was carried out by applying basic statistical criteria and the standard tests of Mann-Whitney, Kruscal-Wallis, and Kolmogorov-Smirnov. The results of these tests, which compare the real with predicted moisture data, demonstrate that the fluidized bed dryer is well-modeled by the ANN (prediction error of 4.5%).

This article describes the selection of the training algorithm of an artificial neural network (ANN) used to model the drying of olive oil mill waste in a fluidized-bed dryer. The ANN used was a three-layer perceptron that predicts the moisture value at time t + T from experimental data (solid moisture, input air, and fluidizedbed temperature) at t time; T is the sampling time. In this study, 14 training algorithms were tested. This selection was carried out by applying several statistical tests to the real and predicted moisture values. Afterward, an experimental design was carried out to analyze the influence of the training function parameters on the ANN performance. Finally, Polak-Ribiere conjugate gradient backpropagation was selected as the best training algorithm. The ANN trained with the selected algorithm predicted the moisture with a mean prediction error of 1.6% and a correlation coefficient of 0.998.