Environmental impact assessment of chicken meat production via an integrated methodology based on LCA, simulation and genetic algorithms

This study performed a Life Cycle Assessment (LCA) to evaluate the environmental impact of chicken meat production from a Mexican case study, with a “cradle-to-slaughterhouse gate” approach. To overcome the LCA's limitations and provide a more holistic picture of the system, simulation and artificial intelligence techniques were integrated. First, raw material/energy requirements were obtained from the case study and simulated using Process simulation (PS) and Monte Carlo (MC) simulation to estimate the emissions and quantify their uncertainty. Then, IMPACT 2002 + was used to calculate the overall impact using Ecoinvent and LCA Food databases. The results highlight that chicken farms are the main factors responsible for the environmental impacts assessed, where feed production (use of chemicals and energy requirements) and on-farm emissions (organic waste decomposition) are the main contributors. Concerning the slaughterhouse, the energy production (electricity and steam) and the cooling-related activities present a significant impact. Afterwards, three impact allocation procedures (mass method, neural networks, and stepwise regression) were tested, showing similar results. Finally, a multiobjective optimization model based on a Genetic Algorithm was applied looking to minimize the environmental impacts and maximize the economic benefits. The selected alternative achieved a reduction of 15.14% per functional unit at the environmental indicators. The results encourage the use of support techniques for LCA to perform a reliable assessment and an environmental/economic optimization of the system

Multi-Objective Optimal Design of a Hydrogen Supply Chain Powered with Agro-Industrial Wastes from the Sugarcane Industry: A Mexican Case Study

This paper presents an optimization modeling approach to support strategic planning for designing hydrogen supply chain (HSC) networks. The energy source for hydrogen production is proposed to be electricity generated at Mexican sugar factories. This study considers the utilization of existing infrastructure in strategic areas of the country, which brings several advantages in terms of possible solutions. This study aims to evaluate the economic and environmental implications of using biomass wastes for energy generation, and its integration to the national energy grid, where the problem is addressed as a mixed-integer linear program (MILP), adopting maximization of annual profit, and minimization of greenhouse gas emissions as optimization criteria. Input data is provided by sugar companies and the national transport and energy information platform, and were represented by probability distributions to consider variability in key parameters. Independent solutions show similarities in terms of resource utilization, while also significant differences regarding economic and environmental indicators. Multi-objective optimization was performed by a genetic algorithm (GA). The optimal HSC network configuration is selected using a multi-criteria decision technique, i.e., TOPSIS. An uncertainty analysis is performed, and main economic indicators are estimated by investment assessment. Main results show the trade-off interactions between the HSC elements and optimization criteria. The average internal rate of return (IRR) is estimated to be 21.5% and average payback period is 5.02 years