Integrated biomass gasification combined cycles can be advantageous for providing multiple products simultaneously. A new electricity and freshwater generation system is proposed based on the integrated gasification and gas turbine cycle as the main system, and a steam Rankine cycle and multi-effect desalination system as the waste heat recovery units. To evaluate the performance of the system, energy, exergy, and economic analyses were performed. Also, a parametric analysis was performed to assess the effects of various parameters on the system’s performance criteria. The economic feasibility of the plant was analyzed in terms of net present value. For the base case, the performance metrics are evaluated as Wnet=8.347 MW, ε=46.22%, SUCP=14.07 $/GJ, and mfw=11.7 kg/s. Among all components of the system, the combustion chamber is the greatest contributor to the exergy destruction rate, at 3250 kW. It is shown with the parametric analysis that raising the combustion temperature leads to higher electricity and freshwater production capacity. For a fuel cost of 2 $/GJ and an electricity price of 0.07 $/kWh, the total net present value at the end of plant’s lifespan is 6.547×106 $, and the payback period is 6.75 years. Thus, the plant is feasible from an economic perspective.

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Based on the benefits of integrated gasification combined cycles (IGCCs), a cogeneration plant for providing electricity and freshwater is proposed. The main novelties of the devised system are the integration of biomass gasification and a regenerative gas turbine with intercooling and a syngas combustor, where the syngas produced in the gasifier is burned in the combustion chamber and fed to a gas turbine directly. The energy discharged from the gas turbine is utilized for further electricity and freshwater generation via Kalina and MED hybridization. The proposed system is analyzed from energy, exergy, exergoeconomic, and reliability–availability viewpoints. The optimal operating condition and optimum performance criteria are obtained by hybridizing an artificial neural network (ANN), the multi-objective particle swarm optimization (MOPSO) algorithm. According to results obtained, for the fourth scenario of the optimization process, optimal values ​​of , , , and are obtained for the exergy efficiency, freshwater production rate, sum unit cost of products, and net output power, respectively. According to reliability and availability assessment, the probability of the healthy working state of all components and subsystems is the system is shown to be available of the time over the 20-year lifetime.

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