Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202507054
In many Chinese wastewater treatment plants (WWTPs), residual chlorine is still manually monitored at low frequencies, leading to imprecise disinfectant dosing. This study systematically compared four machine learning models—backpropagation (BP) neural network, long short-term memory (LSTM) neural network, random forest (RF), and support vector regression (SVR)—for predicting chlorine consumption (i.e., the difference between chlorine dose and residual chlorine) during non-monitoring periods under different residual chlorine monitoring frequencies (every 1, 2, 4, 6, and 8 h). Using data from Plant A (equipped with online residual chlorine monitoring) and Plants B and C (manual monitoring every 6 h and 8 h, respectively), input variables included online water quality indicators (temperature, flow, NH3-N, CODCr, TP, TN) and chlorine dose. Results showed that at 1-h intervals, LSTM achieved the highest prediction accuracy; at 2–4-h intervals, RF performed best; at 6-h or lower frequencies, BP was superior; SVR performed worst across all frequencies. Validation on Plants B and C confirmed BP's optimal performance under low-frequency conditions, and particle swarm optimization (PSO) significantly improved its accuracy. These findings provide a basis for selecting appropriate machine learning models for chlorine consumption prediction under varying monitoring frequencies, particularly low-frequency manual monitoring, thereby supporting precise disinfectant dosing control.
The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.225241
Amid the global pursuit of carbon neutrality, the catalytic conversion of carbon dioxide (CO2) into high-value-added aromatics represents a critical frontier in sustainable chemistry. This process offers the dual benefit of mitigating greenhouse gas emissions while establishing a non-petroleum route for the production of indispensable platform chemicals. However, the practical realization of CO2 conversion is hindered by formidable challenges originating from the thermodynamic stability of CO2 and the kinetic challenges in C-C bond formation. This review provides a critical and comprehensive analysis of recent progress on CO2 hydrogenation to aromatics, focusing on the development of catalyst design, reaction kinetics, and reactor engineering, with the goal of accelerating industrial application. The two dominant reaction pathways, i.e., the methanol-intermediate and the olefin-intermediate routes, are summarized and progress in the design of efficient multifunctional catalysts for each pathway is given. A key point in bifunctional catalyst development is the challenge of balancing the synergy and separation of hydrogenation sites and acidic aromatization active sites. Synergy is crucial for driving the reaction equilibrium forward by rapidly consuming intermediates, whereas separation, often achieved through sophisticated architectures like core-shell structures, is vital for preventing deactivation, such as the migration of alkaline promoters into the zeolite (the aromatization component). Also, this review analyzes the kinetic modeling progress proposed for this complex, multi-step reaction system. For the initial CO2 conversion step, the authors highlighted the evolution of kinetic models, particularly the ongoing efforts to accurately quantify the critical water inhibition effect in methanol synthesis. For the subsequent aromatization stage, this review critically compares two distinct modeling strategies: the use of lumping models, which simplify the reaction network for robust engineering simulations, and the single-event microkinetic (SEMK) models, which offer profound mechanistic insights by considering elementary reaction steps. Furthermore, it is pointed out that these kinetic models serve as indispensable inputs for computational fluid dynamics (CFD) simulations, which guide the design, optimization, and scale-up of industrial reactors. These simulations can address practical engineering challenges such as thermal management to control hotspots and fluid dynamics to mitigate excessive pressure drop. By systematically bridging the conceptual gap from atomic-level catalyst design to macro-scale reactor optimization, this review provides theoretical guidance aimed at accelerating the engineering scale-up of this vital carbon utilization technology.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025011002
Cadmium (Cd) contamination in farmland soils poses a threat to food security, necessitating effective remediation strategies. This study prepared three types of phosphorus-enriched biochar (PBC) from rice straw and different phosphorus sources (fused calcium magnesium phosphate, citric acid-activated fused calcium magnesium phosphate, and monocalcium phosphate) to evaluate their potential in stabilizing Cd(II), releasing phosphorus, and enhancing plant resistance to heavy metal stress. Under Cd stress, PBC amendments significantly improved soil physicochemical properties and reduced Cd bioavailability through direct immobilization (adsorption, precipitation) and indirect mechanisms. Specifically, CBC and MBC treatments reduced DTPA-extractable Cd by 45.73% and 48.92%, respectively. The porous structure of biochar facilitated sustained phosphorus release, influencing soil solution phosphorus dynamics. In pot experiments with Brassica napus L., PBC application significantly improved agronomic traits and reduced oxidative stress markers. The C5 treatment increased leaf area by 100%, M7 increased plant height by 26.8%, and M3 reduced malondialdehyde (MDA) and hydrogen peroxide (H2O2) contents by 72.5% and 61.2%, respectively. These findings demonstrate that PBC effectively alleviates Cd toxicity, promotes plant growth, and enhances stress resistance, offering a feasible strategy for remediating Cd-contaminated soils while providing a sustainable phosphorus source.