Control model of energy flow in agricultural biogas plant using SCADA software
The article presents a model of controlling the flow of energy in agricultural biogas plant. Control model was made on the basis of a shared project biogas power plant. Biogas plant control algorithm modeled using SCADA software and an analysis of cost performance and life of the investment have been presented.
SCADA System for Management and Visualization in Generation of Renewable Energy from Biomass
In the paper is described the project of one of the first SCADA (supervisory control and data acquisition) system for biogas installations in Bulgaria. SCADA is a type of industrial control system (ICS) as computer-controlled system for monitoring and control of technological processes in biogas installations. Development of SCADA forbiogas plants is new scientific and industrial implementations of the remote process control and supervision in generation of renewable energy from biomass. The system is based on the programmable logic controllers (PLC) and human-machine interface (HMI). The system is designed for producing a huge volume of biogas, electricity and thermal energy with not very high amount of investment. The proposed system guarantees high reliability and productivity, low own consumption and modern monitoring and control of the technology parameters.
Biogas Plant Control and Optimization Using Computational Intelligence MethodsBiogasanlagenregelung und -optimierung mit Computational Intelligence Methoden
Abstract The optimization of agricultural and industrial biogas plants with respect to external influences and various process disturbances is essential for efficient plant operation. The fact that most biogas plants are manually operated because of a lack of online-measurements and limited knowledge about the anaerobic digestion process makes it necessary to develop new optimization and control strategies. However, the optimization and control of such plants is a challenging problem due to the underlying highly nonlinear and complex digestion processes. One approach to address this challenge is to exploit the flexibility and power of computational intelligence (CI) methods such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO). The use of CI methods in conjunction with a validated plant simulation model, based on the Anaerobic Digestion Model No. 1, allows optimization of the 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.