Optimization of a polygeneration system for energy demands of a livestock farm

Abstract

A polygeneration system is an energy system capable of providing multiple utility outputs to meet local demands by application of process integration. This paper addresses the problem of pinpointing the optimal polygeneration energy supply system for the local energy demands of a livestock farm in terms of optimal system configuration and optimal system capacity. The optimization problem is presented and solved for a case study of a pig farm in the paper. Energy demands of the farm, as well as the superstructure of the polygeneration system were modelled using TRNSYS software. Based on the locally available resources, the following polygeneration modules were chosen for the case study analysis: a biogas fired internal combustion engine co-generation module, a gas boiler, a chiller, a ground water source heat pump, solar thermal collectors, photovoltaic collectors, and heat and cold storage. Capacities of the polygeneration modules were used as optimization variables for the TRNSYS-GenOpt optimization, whereas net present value, system primary energy consumption, and CO 2 emissions were used as goal functions for optimization. A hybrid system composed of biogas fired internal combustion engine based co-generation system, adsorption chiller solar thermal and photovoltaic collectors, and heat storage is found to be the best option. Optimal heating capacity of the biogas co-generation and adsorption units was found equal to the design loads, whereas the optimal surface of the solar thermal array is equal to the south office roof area, and the optimal surface of the PV array corresponds to the south facing animal housing building rooftop area.

Data-driven decision support in livestock farming for improved animal health, welfare and greenhouse gas emissions: Overview and challenges

“…Computational Fluid Dynamics (CFD) based optimization (Singh and Saxena, 2015) Diet optimization Linear programming and selforganizing migrating genetic algorithm (SOMGA) (Fawaz et al, 2014) Temperature and air quality CO2, NH3 concentrations Chicken Thermal flow, CO2, NH3 concentrations (Halachmi, 2015) Yearly turnover (production) Space/culture volumes Fish Fish growth phases (Mančić et al, 2016) Polygeneration system configuration Temperature, GHG emissions Pig Energy demand, polygeneration system (Zhang et al, 2016) Milk production forecast Climate, Physical aspects of cows Cattle Yield production model, herd (Alqaisi et al, 2017) Nutritional Welfare: There are a few works that combine optimization and simulation for ensuring the health and welfare of livestock. They include the works of Bajardi et al (2012) and Michalak (2019), which deal with epidemic control in livestock farms.…”

“…A similar study by Fawaz et al (2014) provided a simulation-based optimization of heat and ventilation in chicken shelters. Mančić et al (2016) proposed a simulation-based study involving the optimization of configurations of an energy system in order to meet the energy demands of a pig farm. Identifying and controlling epidemic spread among livestock is another area which influences the cost.…”

Modelling the energetic performance of a pig stable

“…One fifth of the global pork production takes place in Europe [1]. Around 24 million tons of pig meat were produced in Europe in 2019 [2].…”

“…Illustration of the influence of the environmental temperature on the body temperature and heat production of pigs. [3] To ensure the comfort of the pigs, the European agricultural sector still heavily relies on fossil energy inputs [1]. In 2018, 84% of the final energy use in farming came from a fossil energy source.…”

Thermo-economic analysis and optimization of the steam absorption chiller network plant

“…Since the thermo-economic analysis aims to investigate the energetic technical parameters and also the financial indices to estimate if any thermal plant would be feasibly or not [18], the needs to search for the ideal input configuration in terms of the energetic and economic domain have been mentioned in many studies on polygeneration systems, as it could be seen in [19,20]. In this context, using different technical methods to optimize the thermal systems have been applied to find the best configuration for energetic and economic efficiency using the Four E technique [21], artificial neural network [22], using the TRNSYS function the optimization of the economic index [23], MOPSO algorithm [24] or even combining different techniques of optimization such as pessimistic and optimistic criteria [25]. Previously, Panahizadeh et al [26] presented an analogous study using the exergetic parameters as the basis of the optimization method trying to minimize the exergy destruction by applying the particle swarm optimization (PSO) algorithm.…”