This study proposed a novel solid oxide fuel cell (SOFC)-integrated system fueled by biogas production from anaerobic fermentation of organic municipal solid wastes. The proposed system composed of the organic Rankine cycle (ORC), the anaerobic digester (AD), the SOFC, Kalina cycle (KC), and the parabolic trough solar collector (PTSC) which provided heat for the continuous and stable operation of anaerobic fermentation. Firstly, the mathematical model was established and then the energy, exergy, and economy analysis were evaluated. The results showed that the total exergy efficiency and the cost rate achieved 30.96% and 19.68$/h under design conditions. It was found that the total exergy efficiency increased as the increasing in the evaporating pressure of ORC and the basic ammonia solution concentration, but it decreased with the increase in the solar radiation intensity. When the input temperature and the current density of SOFC were increased, the total exergy efficiency was increased firstly then decreased and reached the maximum value of 439.2 and 481 kW at the SOFC input temperature of 628.6°C and the current density of 8286 A/m². Besides, the cost rate was increased with the increase of the power consumption of the main components. The Pareto frontier was obtained by using the non-dominated sorting whale optimization algorithm (NSWOA) which was employed to perform the multi-objective optimization, and the comprehensive decision-making method (TOPSIS) was used to get the optimal solution. The optimal solution showed that the total exergy efficiency and the cost rate could reach 39.56% and 14.23 $/h, respectively. Furthermore, in order to reduce optimization time and improve the accuracy of surrogate model, this study examined an improved artificial neural network (ANN) algorithm combining data-driven surrogate model and whale optimization algorithm (WOA) to replace the physical model. It was also found that the time using the developed surrogate model to obtain the optimal solution set spent only 5 minutes which is far less than the physical method which spent more than 40 hours under the same computer configuration.

中文翻译：

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结合人工神经网络和鲸鱼优化算法的新型综合能源系统的能量、火用、经济分析和多目标优化

本研究提出了一种新型固体氧化物燃料电池 (SOFC) 集成系统，该系统由城市有机固体废物厌氧发酵产生的沼气提供燃料。拟议的系统由有机朗肯循环（ORC）、厌氧消化器（AD）、SOFC、卡林纳循环（KC）和抛物面槽式太阳能集热器（PTSC）组成，为厌氧发酵的连续稳定运行提供热量. 首先建立了数学模型，然后进行了能量、火用和经济性分析。结果表明，在设计条件下，总火用效率和成本率分别达到30.96%和19.68$/h。发现总火用效率随着ORC蒸发压力和碱性氨溶液浓度的增加而增加，但随着太阳辐射强度的增加而降低。当SOFC输入温度和电流密度升高时，SOFC输入温度628.6℃、电流密度8286 A/m时，总火用效率先升高后降低，达到最大值439.2和481 kW^2个. 此外，成本率随着主要元器件功耗的增加而增加。采用非支配排序鲸鱼优化算法（NSWOA）进行多目标优化，得到帕累托边界，采用综合决策法（TOPSIS）求得最优解。最优解表明总火用效率和成本率分别达到39.56%和14.23 $/h。此外，为了减少优化时间并提高代理模型的准确性，本研究研究了一种改进的人工神经网络（ANN）算法，结合数据驱动的代理模型和鲸鱼优化算法（WOA）来替代物理模型。