Optimization of Biogas Production Process in a Wastewater Treatment Plant

Sludge is a byproduct of wastewater processing suitable for biogas production. The biogas consisting of about 60% methane can be used to generate electricity and heat. A data-driven approach for optimization of biogas production process in a wastewater treatment plant is presented. The process model is developed using routinely collected data categorized as controllable and uncontrollable variables. Process temperature, total solids, volatile solids, and pH value were selected as controllable variables for the model. The uncontrollable variables include sludge flow rate, organic load, and detention time. A multi-layer perceptron neural network is applied to construct the optimization model. Due to high computational complexity of the model, a particle swarm optimization algorithm is used to maximize the biogas production by finding the optimal settings of controllable variables. The model solution has resulted in a 20.8% increase of the biogas production under optimized operation conditions.