Comprehensive experimental study on microbial desalination cells for biological wastewater recovery, seawater brine treatment, and simultaneous power generation: applications of machine learning and thermal image processing

IF 7 2区工程技术 Q1 ENERGY & FUELS

Sustainable Energy Technologies and Assessments Pub Date : 2025-09-26 DOI:10.1016/j.seta.2025.104608

Mohammad Hasan Khoshgoftar Manesh, Maedeh Saneetaheri, Seyed Alireza Mousavi Rabeti

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Abstract

Microbial Desalination Cells (MDCs) have emerged as a groundbreaking solution for addressing water scarcity, wastewater treatment, and sustainable energy production simultaneously. In this study, we present an advanced experimental and analytical approach to optimize MDC performance by integrating machine learning predictions, thermal imaging analysis, and electrochemical monitoring under diverse environmental conditions. We conducted five distinct tests using wastewater from urban treatment plants and saline water from the Caspian Sea and the Persian Gulf to examine the interplay of key operational parameters, including salinity, COD, pH, TDS, internal resistance, and polarization behavior. The results reveal several significant insights. Higher initial salinity substantially enhances desalination efficiency, achieving a record salt removal rate of 96 %. We identified an inverse relationship between COD removal and desalination efficiency, indicating a trade-off between organic matter degradation and salt removal. Oxygen availability proved to be a critical determinant of MDC performance; its presence increased voltage significantly, peaking at 1099 mV. Power generation reached its maximum at an optimal current density, producing a peak power density of 0.143 mW/cm².Thermal imaging analysis uncovered a direct correlation between heat distribution, ion migration, and microbial activity, offering valuable insights into system efficiency and energy losses. The integration of machine learning models yielded highly accurate predictions, closely matching experimental data and providing a scalable pathway for MDC performance optimization. Collectively, these findings establish MDCs as a transformative technology for renewable water and energy solutions with strong potential for real-world applications.

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生物废水回收、海水盐水处理及同步发电的微生物脱盐电池综合实验研究：机器学习和热图像处理的应用

微生物海水淡化电池（MDCs）已经成为同时解决水资源短缺、废水处理和可持续能源生产的突破性解决方案。在这项研究中，我们提出了一种先进的实验和分析方法，通过集成机器学习预测，热成像分析和电化学监测在不同环境条件下优化MDC性能。我们使用来自城市处理厂的废水和来自里海和波斯湾的盐水进行了五种不同的测试，以检查关键操作参数的相互作用，包括盐度、COD、pH、TDS、内阻和极化行为。研究结果揭示了几个重要的见解。较高的初始盐度大大提高了海水淡化效率，达到了创纪录的96%的脱盐率。我们确定了COD去除率与脱盐效率之间的反比关系，表明有机物降解和脱盐之间存在权衡关系。氧气供应被证明是MDC性能的关键决定因素；它的存在显著增加了电压，峰值在1099 mV。在最佳电流密度下，发电量达到最大，峰值功率密度为0.143 mW/cm2。热成像分析揭示了热分布、离子迁移和微生物活动之间的直接关系，为系统效率和能量损失提供了有价值的见解。机器学习模型的集成产生了高度准确的预测，与实验数据密切匹配，并为MDC性能优化提供了可扩展的途径。总的来说，这些发现确立了MDCs作为可再生水和能源解决方案的变革性技术，具有在现实世界应用的巨大潜力。

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来源期刊

Sustainable Energy Technologies and Assessments Energy-Renewable Energy, Sustainability and the Environment

CiteScore

12.70

自引率

12.50%

发文量

1091

期刊介绍： Encouraging a transition to a sustainable energy future is imperative for our world. Technologies that enable this shift in various sectors like transportation, heating, and power systems are of utmost importance. Sustainable Energy Technologies and Assessments welcomes papers focusing on a range of aspects and levels of technological advancements in energy generation and utilization. The aim is to reduce the negative environmental impact associated with energy production and consumption, spanning from laboratory experiments to real-world applications in the commercial sector.