Enhancement of the capability in wastewater treatment by adding biochar into a conical-shaped sequencing batch reactor

2025-10-21T13:46:11+07:00

The present study evaluates the manner in which reactor geometry and corn-cob biochar (CCBC) collectively enhance the performance of sequencing batch reactors (SBRs). Four bench-scale SBRs (1.5 L) were operated in parallel, initially in cylindrical vessels and subsequently in conical vessels, followed by CCBC addition (1.5 g and 3.0 g per reactor, with weekly replenishment). Water quality parameters (TOC, NH4+−N, NO2−−N, NO3−−N, TN), DO, and MLSS were monitored, and FE-SEM was used to confirm biofilm development on CCBC. In conditions of identical operating cycles, the conical SBR exhibited a higher TOC removal rate (95 ± 2%, effluent 6.7 mg-C/L) in comparison to the cylindrical SBR (90 ± 2%, 12.8 mg-C/L). This is indicative of an estimated effluent BOD of 26.3 mg/L as opposed to 49.9 mg/L. The effluent total nitrogen (TN) levels exhibited a decline from 21.0 mg-N/L (cylindrical, 3 cycles/day) to 9.6 mg-N/L (conical, 3 cycles/day) and 9.2 mg-N/L (conical, 4 cycles/day). Following the CCBC addition, a marked decrease in effluent TOC was observed (1.1 and 2.2 mg-C/L for 1.5 g and 3.0 g, respectively), which then stabilized at low levels (3.5 and 2.9 mg-C/L on average). Concurrently, there was a significant reduction in , NH4+−N, NO2−−N, NO3−−N,, and TN relative to the control groups. A TN mass balance indicated that adsorption contributed only marginally to long-term T-N removal (< 0.1%), with denitrification instead predominating. This was likely promoted by larger anoxic zones in the conical geometry and by CCBC-supported microhabitats. FE-SEM analysis confirmed progressive biofilm colonization on CCBC. This study demonstrated for the first time that synergistic functions are achieved by placing a high-performance carrier, which combines macro-pores suitable for microbial habitation with high organic matter adsorption capacity, into a conical reactor with excellent flow and sedimentation stabilization. This method is considered to be a highly cost-effective approach for the removal of nutrients and organic matter via SBR in decentralised wastewater treatment systems in small-scale facilities and local hospitals in developing countries.

A hybrid machine learning and SCAPS 1D expedition for optimization of lead-free BeSiP₂ perovskite solar cells

2025-11-22T15:43:51+07:00

The chalcopyrite-type semiconductor BeSiP₂ has recently emerged as a promising lead-free absorber candidate for thin-film photovoltaics due to its direct bandgap, strong visible-light absorption, and high carrier mobility. However, a systematic understanding of its device-level behavior and optimization strategy remains limited. In this study, a comprehensive physics–data hybrid framework is developed to investigate the FTO/NiO/BeSiP₂/TiO₂/C–Cu solar cell, integrating SCAPS-1D simulations with machine learning (ML) based prediction and feature interpretation. The numerical analysis reveals that device performance is highly sensitive to transport-layer thicknesses, contact resistances, temperature, and illumination intensity. An optimized structure achieves a power conversion efficiency (PCE) of 22.94%, Voc = 0.89 V, Jsc = 31.0 mA cm⁻², and FF = 83%. The results indicate that high shunt resistance (Rsh) and suitable absorber thickness (~800 nm) maximize efficiency by suppressing recombination and enhancing charge transport. Ensemble learning models XGBoost and Random Forest were trained on simulation data, achieving R² > 0.99 with minimal prediction error. Both models identified Rsh, illumination intensity, and absorber thickness as dominant contributors to η, aligning closely with device physics. The proposed SCAPS–ML hybrid methodology enables accurate prediction and interpretability of photovoltaic trends, offering a scalable pathway for optimizing lead-free BeSiP₂-based solar cells and other emerging thin-film technologies.

Maejo International Journal of Energy and Environmental Communication

Enhancement of the capability in wastewater treatment by adding biochar into a conical-shaped sequencing batch reactor

A hybrid machine learning and SCAPS 1D expedition for optimization of lead-free BeSiP₂ perovskite solar cells