Artificial intelligence driven process optimization for cleaner production of biomass with co-valorization of wastewater and flue gas in an algal biorefinery

Abstract

In the present study, an optimized process involving integrated events of flue gas CO2 sequestration and wastewater utilization was developed for improved microalgal biomass production in a cleaner fashion. An artificial neural network (ANN) combined with genetic algorithm (GA) optimization tool was employed for predicting optimal process conditions for enhancing the biomass of the green microalga, Scenedesmus sp., using domestic wastewater as culture medium and coal-fired flue gas as carbon source in an integrated process chain. A 24 central composite design involving four independent process parameters such as light intensity, photoperiod, temperature and initial pH as input variables with biomass productivity as the output was used to construct a non-linear ANN model followed by GA tool to predict optimal combinations of process conditions. Among the tested neural network architecture, 4-12-1 ANN topology was found to be the optimal network architecture in terms of maximum correlation coefficient (R = 0.9947) and minimum mean square error as the performance indexes. On employing the optimized ANN model as fitness function in GA tool, the optimum values of the process parameters for efficient biomass production were as follows: light intensity = 124 μmol m−2 s−1, photoperiod; L:D = 17:7 (h), temperature = 27.5 °C and initial pH = 9.5. These parameters improved the algal biomass productivity by about 57%, CO2 sequestration rate of 578.1 ± 23.1 mg L−1 d−1 and chemical oxygen demand (COD) reduction of 95.9 ± 2.4% were achieved. The optimized process yielded biomass with lipid content and productivity of 34.6% and 106.4 mg L−1 d−1 respectively. The biofuel assessment from the fatty acid methyl ester profile obtained also conformed to the international standard specifications for biodiesel.