Deterministic Optimization Approach for High-Performance Externally Fired Biomass-Fueled Micro-Gas Turbines

Abstract

This work focuses on a dual-objective optimization of a 100 kWe externally fired micro-gas turbine utilizing the producer gas from a biomass gasifier. Although externally fired micro-gas turbines are convenient for resolving operability issues in biomass combined heat and power applications, these configurations are still lacking in efficiency compared to the commercial natural-gas fired microturbines. The main cause is the material temperature limitations in the recuperator and the current uneconomical use of high-temperature resistance materials. Toward the achievement of higher efficiency by keeping system economic viability, an optimization process is followed based on the Normal Constraint Method, which generates evenly distributed solutions of a Pareto front. The selected method can determine high-performance solutions, being unidentified by one-dimensional approaches, providing information about the distribution of critical cycle parameters, across the complete objective space by the evaluation of a relatively small set of Pareto points. These critical parameters are the pressure ratio, the recuperator temperature difference, and maximum temperature. The exergetic efficiency and the relative recuperator cost are the optimization objectives. The deterministic Nelder–Mead algorithm is used for the acquisition of Pareto solutions, along with a penalty-based method to perform the constrained optimization. The implemented optimization method can identify superior solutions compared to one-dimensional approaches, as the latter result in higher recuperator costs around 41–112% at the same exergetic efficiency, revealing that high-performance is not only restricted by the recuperator but also by the compressor operating range.