Modelling of wastewater treatment, microalgae growth and harvesting by flocculation inside photo bioreactor using machine learning technique

Abstract

Biological wastewater treatment with the use of algae-bacteria consortia for the uptake of nutrient and recovery of resource is considered as the ‘paradigm shift’ from the process of mainstream wastewater treatment plants (WWTPs) so as to mitigate the pollution and thus promoting the circular economy. In this regard, the application of machine learning algorithms (MLAs) was found to be effectual and beneficial for the prediction of uncertain performances in the process of treatment and it shows a satisfactory result for the effective optimization, monitoring, uncertainty prediction and so on in the environment systems. The proposed approach aims at modelling the treatment of wastewater, growth of micro algae and flocculation harvesting at the photobioreactor (PBR) along with the utilization of machine learning techniques. Initially, the raw data from the PBR was taken and is pre-processed using z-score normalization technique followed by extraction and selection of features that are more appropriate. The Adaptive neuro-fuzzy inference system (ANFIS) model is built along with the modified Fuzzy C-Means algorithm (MFCM) so as to cluster the huge amount of data. ANFIS is employed for the estimation of controller output parameters and for controlling the temperature inside the reactor. The output controller parameter performance can be enhanced by the use of optimization approach. The discrete Multilayer perceptron (DMLP) with the hyper tuning parameters of Iterative Levi’s Flight Dependent Cuckoo search optimization algorithm (ILF-CSO) is employed for the prediction purpose of attained cultivation growth rate and the pH of treated wastewater. The optimization technique based on machine learning model in turn offers the best possible solution needed for the estimation of output parameters. Thus, the removal rate of effluent T-N concentrations from the wastewater treatment is predicted with some intervals of day. At last, the performance is estimated in terms of growth rate, temperature variations, biomass, nitrate and phosphate concentrations, and error rates (RMSE, APE), and determination coefficient (R2). The attained outcome shows that the presented model is effectual and has the potential to apply for controlling and predicting the biological wastewater treatment plants.