A sustainable optimal biomass waste-driven CCHP system to boost the nearly zero energy building concept

The aim of this work is focused on design of a sustainable tri-generation system driven by a biomass (MSW: municipal solid waste) externally-fired gas turbine cycle and utilizing a double-effect absorption chiller/heater. A robust framework based on energy, eco-exergy, and environmental analyses is developed to access the reliability of this proposal. An innovative optimization approach is then applied to reach the optimal sizing and operating conditions of the designed system. The novel optimization procedure is based on the combination of an Artificial Neural Network and multi-criteria Salp Swarm Algorithm. To make the outputs of the study applicable, a case study building is selected and the developed models are applied for satisfying its electrical, heating, and cooling demands. The building simulation is done with detailed real data and assumptions using by powerful energy architecture software. The results illustrated that the mass flow rate of MSW is the most effective variable on the system performance followed by the compressor pressure ratio. The eco-exergy analysis revealed that almost 40% and 23% of system's total cost is respectively related to gas turbine and gasifier. At the optimal operation, the system could produce 541 kW of electricity, 2052 kW of heat, and 2650 kW of cold. The levelized cost of electricity generation is obtained as 0.083 $/kWh with environmental factor of 1.33 kgCO2/kWh.

Automated summary

The aim of this work is to design a sustainable tri-generation system driven by a biomass externally-fired gas turbine cycle and utilizing a double-effect absorption chiller/heater. The study develops a robust framework based on energy, eco-exergy, and environmental analyses to evaluate the reliability of this proposal. An innovative optimization approach is used to determine optimal sizing and operating conditions, combining an Artificial Neural Network and multi-criteria Salp Swarm Algorithm. The results demonstrate the system's ability to meet the electrical, heating, and cooling demands of a case study building, with the mass flow rate of MSW and compressor pressure ratio identified as the most influential variables on system performance.