The increasing demand for solar energy drives the mass production of diverse photovoltaic (PV) systems and, consequently, the growth of used solar panels and their environmental footprint. This study applied a new hybrid optimization method based on particle swarm and ant colony optimization algorithms to solve the problems of PV module toxicity. The Weibull distribution function was used to measure the service life of PV modules under a variety of failure scenarios. The simulation results show that PV modules that were guaranteed to have the service life of 25–30 years mostly last 20–25 years. The toxicity coefficient and the use of a hybrid method suggest that the time period when a solar module exhibits a maximum efficiency with a minimal environmental footprint ranges from 15 to 20 years. It was established that this interval corresponds to the level at which the amount of waste does not exceed the amount of energy generated with a minimum number of failures. The proposal will be effective in predicting the performance of solar systems. This approach can be improved in terms of cost and benefit and employed in the future research on renewable energy and ecosystems.

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基于蚁群优化算法的混合方法在太阳能电池技术环境影响多因素分析中的应用

对太阳能的日益增长的需求推动了各种光伏（PV）系统的大规模生产，因此推动了废旧太阳能电池板及其环境足迹的增长。本研究应用了一种新的基于粒子群和蚁群优化算法的混合优化方法来解决光伏组件毒性问题。威布尔分布函数用于测量各种故障情况下光伏组件的使用寿命。仿真结果表明，被保证具有25–30年使用寿命的PV组件大多数可以持续20–25年。毒性系数和混合方法的使用表明，太阳能电池组件以最小的环境足迹展现出最大效率的时间范围为15到20年。可以确定的是，该时间间隔对应于这样的级别，在该级别上，浪费的数量不超过产生最少故障次数的能量生成量。该建议将有效地预测太阳能系统的性能。可以在成本和收益方面改进此方法，并将其用于可再生能源和生态系统的未来研究中。