Ensemble machine learning approach for examining critical process parameters and scale-up opportunities of microbial electrochemical systems for hydrogen peroxide production

Advances in sulfate-reducing bacteria-driven bioelectrolysis: mechanisms and applications in microbial electrolysis cell technology

The discharge of sulfate-rich wastewater from chemical and pharmaceutical and food processing industries results in serious environmental problems that impact both the natural environment and human health. The conventional sulfate removal process using chemical precipitation consumes much energy and results in the production of additional pollutants that decrease its scalability and treatment performance. Microbial electrolysis cells (MECs) using sulfate-reducing bacteria (SRB) is a promising sustainable technology for treating wastewater and recovering resources because the metabolic process of SRB in MECs can convert sulfate to sulfide while the cells also produce bioenergy through electrochemical processes. This review focuses on the processes of sulfate reduction in MECs that have demonstrated potential for sulfate removal and hydrogen production and heavy metal elimination and organic pollutant degradation. This review also systematically discussed machine learning systems that optimize MECs performance and result prediction and efficiency enhancement. The SRB-MECs systems have two advantages by producing clean energy while treating wastewater that makes them suitable for application in industrial processes. The two main challenges for the implementation of these systems are the scalability of the system and its long-term operational reliability. This review highlights the need for more research to enhance system performance and microbial efficiency and accelerate the practical implementation of SRB-MECs technology as a sustainable and energy-efficient solution for treating industrial effluents.

Potential and challenge in accelerating high-value conversion of CO2 in microbial electrosynthesis system via data-driven approach

Microbial electrosynthesis for CO2 utilization (MESCU) producing valuable chemicals with high energy density has garnered attention due to its long-term stability and high coulombic efficiency. The data-driven approaches offer a promising avenue by leveraging existing data to uncover the underlying patterns. This comprehensive review firstly uncovered the potentials of utilizing data-driven approaches to enhance high-value conversion of CO2 via MESCU. Firstly, critical challenges of MESCU advancing have been identified, including reactor configuration, cathode design, and microbial analysis. Subsequently, the potential of data-driven approaches to tackle the corresponding challenges, encompassing the identification of pivotal parameters governing reactor setup and cathode design, alongside the decipheration of omics data derived from microbial communities, have been discussed. Correspondingly, the future direction of data-driven approaches in assisting the application of MESCU has been addressed. This review offers guidance and theoretical support for future data-driven applications to accelerate MESCU research and potential industrialization.

Advancing H2O2 electrosynthesis: enhancing electrochemical systems, unveiling emerging applications, and seizing opportunities

Hydrogen peroxide (H2O2) is a highly desired chemical with a wide range of applications. Recent advancements in H2O2 synthesis center on the electrochemical reduction of oxygen, an environmentally friendly approach that facilitates on-site production. To successfully implement practical-scale, highly efficient electrosynthesis of H2O2, it is critical to meticulously explore both the design of catalytic materials and the engineering of other components of the electrochemical system, as they hold equal importance in this process. Development of promising electrocatalysts with outstanding selectivity and activity is a prerequisite for efficient H2O2 electrosynthesis, while well-configured electrolyzers determine the practical implementation of large-scale H2O2 production. In this review, we systematically summarize fundamental mechanisms and recent achievements in H2O2 electrosynthesis, including electrocatalyst design, electrode optimization, electrolyte engineering, reactor exploration, potential applications, and integrated systems, with an emphasis on active site identification and microenvironment regulation. This review also proposes new insights into the existing challenges and opportunities within this rapidly evolving field, together with perspectives on future development of H2O2 electrosynthesis and its industrial-scale applications.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.