Monitoring and control of biological textile wastewater treatment using artificial neural networks

Abstract

This thesis is concerned with the development of an artificial neural network based control scheme (ANNBCS) to improve the performance of a combined anaerobic and aerobic treatment process for textile industrial effluents. The ANNBCS acquired the required input data from on-line sensors, processed this information and when necessary, suggested suitable remedial action(s) for the treatment process. The objective of the ANNBCS was to take remedial actions that would ensure consistent treatment efficiency whilst meeting discharge consents and reducing operation costs.The most appropriate types of artificial neural networks (ANNs) were selected for use in the control scheme from tests on a range of ANNs. The analysis was carried out with data that was obtained from a fluidised bed anaerobic digester fed with a synthetic baker's yeast wastewater (from another project). The data reflected various operating conditions of the digester such as: steady state, sudden changes in the organic load and sensor failure conditions. The networks that were investigated included the linear, back propagation (BP), radial basis function (RBF), Elman, and self-organising map (SOM). The following criteria were used to select the best performing ANN: (i) accuracy of the network predictions; (ii) time required for the necessary training; (iii) the size of the training data.The off-line predictions made by each ANN were accurate enough to be used although a feed forward (FF) multi-layer Perceptron (MLP) network trained with a BP algorithm proved to be the most suitable candidate. The control scheme also incorporated a SOM whose function was to classify the incoming data before passing the information to an appropriately trained BP network.A comprehensive set of experiments were conducted on a 30 1 up-flow anaerobic sludge blanket (UASB) reactor, in conjunction with a 20 1 aerobic tank, and a 3.75 1 aerobic settler using a cotton simulated textile effluent (STE). The STE included among other components, a sizing agent (potato starch) and a reactive red azo dye. The experiments were designed to define the most appropriate on-line measurements and also remedial actions to be taken by the ANNBCS. The experiments consisted of operating both processes systematically under varying organic and colour load conditions.Part of the data gathered from the described experiments was used to train and test offline, in a computing environment, four control schemes in a progressive manner in order to see which one would better cope with sensor loss. Preliminary results demonstrated that a hybrid structure containing a learning vector quantization (LVQ) (replacing the SOM) followed by a series of BP networks was the most efficient of those tested at dealing with different load conditions whilst being least influenced by sensor failure.Subsequent to the comprehensive set of experiments described above, the ANNBCSs were tested on-line. One experiment controlled a colour step change in load and BA for the UASB reactor (i.e. LVQ + BPs) and the second controlled an organic step change in load for the aerobic stage, hi the last case only BP networks were used since there was no need for a classification network. Further evaluation of the ANNBCS capabilities, namely the response to an organic step change in load, took place in simulation using neural network auto-regressive exogenous (NNARX) models built to represent the UASB reactor during particular organic and colour loads. This testing further demonstrated the robustness of the ANNBCS.<br/