Scholarlink

<p>Accurate prediction of biogas production is essential for optimizing process performance, enhancing system stability, and enabling efficient resource management in bioenergy applications. The integrated microbial electrolysis cell and anaerobic digestion (MECAD) system is a novel technology that enables higher energy recovery through the application of external voltage, offering advantages over conventional anaerobic digestion (AD). Due to the energy-intensive nature of MECAD, optimizing biogas production is particularly important to ensure energy efficiency and system feasibility. In this study, the biogas production rate of a MECAD system was predicted using two machine learning (ML)-based models: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The input variables of the models-including pH, oxidation–reduction potential (ORP), total solids (TS) and volatile solids (VS) removal, hydraulic retention times (HRT), organic loading rates (OLR), applied voltage, and current production (CP)-were collected from a MECAD reactor fed with cattle manure and operated under different conditions. The ANN model achieved R<sup>2</sup> values of 0.9844 for the testing dataset and 0.9760 for the overall dataset, with corresponding biogas production rates of 171&#xa0;mL/day and 204&#xa0;mL/day, respectively. The index of agreement (IA) was 0.9960 for testing and 0.9939 for overall data. Similarly, the factor of two (FA2) values were 0.9962 (testing) and 0.9956 (overall), while the mean bias (MB) was calculated as 10.05&#xa0;mL/day for testing and 8.52&#xa0;mL/day for overall data. In comparison, the ANFIS model yielded R<sup>2</sup> values of 0.9811 (testing) and 0.9774 (overall), with RMSE values of 188&#xa0;mL/day and 198&#xa0;mL/day, respectively. The IA values were 0.9952 and 0.9943; FA2 values were 0.9962 and 0.9987; and MB values were 6.81&#xa0;mL/day and 2.91&#xa0;mL/day for testing and overall datasets, respectively. The results demonstrated that both machine learning-based models effectively and accurately predicted the biogas production in a laboratory-scale MECAD reactor utilizing cattle manure as the substrate.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Artificial intelligence-based modeling of biogas production in a combined microbial electrolysis cell-anaerobic digestion system using artificial neural networks and adaptive neuro-fuzzy inference system

  • 期刊论文

  • 发表日期：

    2025年4月1日

查看全文
  • Fatih Tufaner,
  • Kenan Dalkılıç,
  • Ayşenur Uğurlu

摘要

Accurate prediction of biogas production is essential for optimizing process performance, enhancing system stability, and enabling efficient resource management in bioenergy applications. The integrated microbial electrolysis cell and anaerobic digestion (MECAD) system is a novel technology that enables higher energy recovery through the application of external voltage, offering advantages over conventional anaerobic digestion (AD). Due to the energy-intensive nature of MECAD, optimizing biogas production is particularly important to ensure energy efficiency and system feasibility. In this study, the biogas production rate of a MECAD system was predicted using two machine learning (ML)-based models: an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The input variables of the models-including pH, oxidation–reduction potential (ORP), total solids (TS) and volatile solids (VS) removal, hydraulic retention times (HRT), organic loading rates (OLR), applied voltage, and current production (CP)-were collected from a MECAD reactor fed with cattle manure and operated under different conditions. The ANN model achieved R2 values of 0.9844 for the testing dataset and 0.9760 for the overall dataset, with corresponding biogas production rates of 171 mL/day and 204 mL/day, respectively. The index of agreement (IA) was 0.9960 for testing and 0.9939 for overall data. Similarly, the factor of two (FA2) values were 0.9962 (testing) and 0.9956 (overall), while the mean bias (MB) was calculated as 10.05 mL/day for testing and 8.52 mL/day for overall data. In comparison, the ANFIS model yielded R2 values of 0.9811 (testing) and 0.9774 (overall), with RMSE values of 188 mL/day and 198 mL/day, respectively. The IA values were 0.9952 and 0.9943; FA2 values were 0.9962 and 0.9987; and MB values were 6.81 mL/day and 2.91 mL/day for testing and overall datasets, respectively. The results demonstrated that both machine learning-based models effectively and accurately predicted the biogas production in a laboratory-scale MECAD reactor utilizing cattle manure as the substrate.

Graphical Abstract