Biogas plant optimization using Genetic Algorithms and Particle Swarm Optimization

The optimization of agricultural biogas plants with respect to external influences and various process disturbances is essential for efficient plant operation. However, the optimization and control of such plants is a challenging problem due the underlying highly nonlinear and complex digestion processes. One approach to addressing this challenge is to exploit the flexibility and power of computational intelligence methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). In this paper these methods are used in conjunction with a validated plant simulation model to optimize substrate feed mix, a key factor in stable and efficient biogas production. Results show that an improvement of up to 20% in biogas production and substrate reduction can be achieved when compared to conventional manual operation. In addition, a comparison of the performance of GAs and PSO reveals that while both methods can achieve comparable results PSO has faster convergence and hence is preferred for this application.