Chemical Engineering Journal Advances最新文献_第4页

Carbon capture, utilization, and storage: Scientific basis, practical applications, and climate role 碳捕获、利用和封存：科学基础、实际应用和气候作用

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2026-01-21 DOI: 10.1016/j.ceja.2026.101049

Diane Mariella Maqui, Angelo Earvin Sy Choi

Carbon Capture Utilization and Storage (CCUS) has gained renewed attention as coal continues to dominate global energy systems despite escalating climate concerns. This review outlines the scientific foundations, integration pathways, and practical viability of CCUS in coal-fired applications, focusing on post-combustion, pre-combustion, and oxyfuel processes. It examines emerging utilization technologies that convert captured carbon into industrial and construction inputs. CO₂ transport behavior depends heavily on pressure, temperature, and impurity content; imbalances in these variables trigger phase transitions among liquid, gas, and supercritical states. The review draws from modeled trajectories by the Intergovernmental Panel on Climate Change (IPCC) and International Energy Agency (IEA), aligning CCUS with national strategies in the United States, China, and India. CCUS enables emission reductions in emission-intensive sectors and lowers carbon intensity in thermal power generation. High costs, regulatory uncertainty, and fragmented policy and infrastructure constrain its large-scale deployment. This study presents a unified modification strategy that integrates durability, photothermal responsiveness, and self-cleaning behavior into a scalable and cost-effective graphite felt substrate. The approach transcends conventional single-property optimization by offering a multifunctional surface capable of sustained adsorption and thermal recovery under direct sunlight, marking a step toward practical and economically viable environmental remediation applications.

尽管气候问题日益严重，但随着煤炭继续主导全球能源系统，碳捕集利用与封存（CCUS）重新受到关注。本文概述了CCUS在燃煤应用中的科学基础、整合途径和实际可行性，重点介绍了燃烧后、燃烧前和含氧燃料过程。它考察了将捕获的碳转化为工业和建筑投入的新兴利用技术。CO₂输运行为在很大程度上取决于压力、温度和杂质含量；这些变量的不平衡触发了液体、气体和超临界状态之间的相变。该报告借鉴了政府间气候变化专门委员会（IPCC）和国际能源署（IEA）的模拟轨迹，使CCUS与美国、中国和印度的国家战略保持一致。CCUS可以减少排放密集型行业的排放，降低火力发电的碳强度。高昂的成本、监管的不确定性以及支离破碎的政策和基础设施限制了其大规模部署。本研究提出了一种统一的改性策略，该策略将耐久性、光热响应性和自清洁行为整合到可扩展且具有成本效益的石墨毡基板中。该方法超越了传统的单一性能优化，提供了一种多功能表面，能够在阳光直射下持续吸附和热回收，标志着向实际和经济可行的环境修复应用迈出了一步。

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引用次数: 0

Synergistic effects of a zirconium doped stannate-carbon nitride nanocomposite on design of electrochemical sensor for sensitive detection of the antiandrogen drug flutamide 锆掺杂锡-氮化碳纳米复合材料对抗雄激素药物氟他胺灵敏检测电化学传感器设计的协同效应

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1016/j.ceja.2025.100981

Chandran Bhuvaneswari , Ponnaiah Sathish Kumar , Arumugam Elangovan , Ganesh Arivazhagan , Young-Ki Kim

Flutamide (FLT), an anti-androgen drug widely used in anti-cancer therapy, can cause serious hepatotoxic side effects at high doses. However, released from industrial and hospital effluents, FLT is not fully removed by conventional water treatments, resulting in contamination of water sources that threatens aquatic ecosystems and thus human health. Therefore, there has been a pressing need for sensitive and reliable FLT monitoring, but existing techniques are often costly, labor-intensive, and complex, limiting their practicality. To address this challenge, herein we report the novel design of electrochemical sensor based on a new class of electrode modifier, ZS-CN nanocomposite, synthesized by integrating zirconium (Zr)-doped SnO₂ nanoparticles (ZS) onto 2D graphitic carbon nitride nanosheets (CN). Especially, we introduce Zr as a new dopant in SnO₂ and reveal that the correlative coupling of ZS with porous CN allows for their intimate interfacial contact that promotes efficient electron transfer and electrocatalytic activity, while keeping the functional groups of both components active. As a result, the electrochemical sensor designed by the ZS-CN nanocomposite coated glassy carbon electrode demonstrates an outstanding level of selectivity and sensitivity (1.6553 µA µM⁻¹ cm⁻²) for FLT with a low detection limit (0.009 µM) and a wide detection linear range (0.04–1166 µM), alongside robust reproducibility, stability, and applicability in real-world samples (e.g., human urine and river water). Furthermore, its cyclic voltammetric responses provide mechanistic insights into the correlation between multi-electron redox process and FLT transformation pathways, informing future interfacial engineering strategies for designing versatile electrochemical systems for pharmaceutical pollutant monitoring.

氟他胺（FLT）是一种广泛用于抗癌治疗的抗雄激素药物，大剂量时可引起严重的肝毒性副作用。然而，从工业和医院的废水中释放出来的浮油不能通过常规水处理完全去除，导致水源受到污染，威胁到水生生态系统，从而威胁到人类健康。因此，迫切需要对FLT进行敏感和可靠的监测，但是现有的技术往往成本高、劳动密集且复杂，限制了它们的实用性。为了解决这一挑战，本文报道了一种基于新型电极改性剂ZS-CN纳米复合材料的电化学传感器的新设计，该复合材料是通过将锆（Zr）掺杂的SnO2纳米颗粒（ZS）集成到二维石墨氮化碳纳米片（CN）上合成的。特别是，我们在SnO2中引入了Zr作为新的掺杂剂，并发现ZS与多孔CN的相关耦合允许它们之间的密切界面接触，促进有效的电子转移和电催化活性，同时保持两组分的官能团的活性。因此，由ZS-CN纳米复合涂层玻碳电极设计的电化学传感器对FLT具有出色的选择性和灵敏度（1.6553 μ a μ M−1 cm−2），具有低检测限（0.009 μ M）和宽检测线性范围（0.04-1166 μ M），同时具有强大的再现性，稳定性和在实际样品（例如人类尿液和河水）中的适用性。此外，它的循环伏安响应为多电子氧化还原过程和FLT转化途径之间的相关性提供了机制见解，为未来设计用于药物污染物监测的多功能电化学系统提供了界面工程策略。

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引用次数: 0

Modeling of the Ni(II) removal from aqueous solutions by ion exchange resin: Comparison of various machine learning approaches 离子交换树脂去除水溶液中Ni（II）的建模：各种机器学习方法的比较

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-03 DOI: 10.1016/j.ceja.2025.100987

Shahrzad Maleki , Maryam Mousavifard , Ayoub Karimi-Jashni

This study aims to investigate the removal of Ni(II) ions from aqueous solutions using an ion exchange resin, focusing on various machine learning approaches to predict the process. The research highlights the efficiency of Amberlite IR120 Na as a strong acidic cation exchange resin, examining its adsorption capacity under varying conditions, including resin dose, initial Ni(II) concentration, solution pH, temperature, and contact time. The adsorption kinetics were accurately described by the pseudo-second-order kinetic model. Additionally, both surface adsorption and intra-particle diffusion played roles in the steps of the adsorption rate. The adsorption isotherm data fitted well with the Langmuir model, indicating a maximum adsorption capacity of 134.8 mg/g. Moreover, machine learning techniques were utilized to predict the resin’s performance, evaluating five diverse models: Support Vector Regression (SVR), Random Forest, Decision Tree, Multi-Layer Perceptron (MLP), and Polynomial Regression. The results showed that the SVR model performed better than the others, with a training R² of 0.990 and testing R² of 0.973, along with the lowest mean absolute error and mean squared error. These findings demonstrate the effectiveness of machine learning in accurately modeling the complex relationships within the adsorption process, thus offering valuable insights for optimizing heavy metal removal from wastewater.

本研究旨在研究使用离子交换树脂从水溶液中去除Ni（II）离子，重点关注各种机器学习方法来预测这一过程。本研究强调了Amberlite IR120 Na作为强酸性阳离子交换树脂的效率，考察了其在不同条件下的吸附能力，包括树脂剂量、初始Ni（II）浓度、溶液pH、温度和接触时间。拟二级动力学模型准确地描述了吸附动力学。此外，表面吸附和颗粒内扩散对吸附速率的变化都有影响。吸附等温线数据与Langmuir模型拟合良好，最大吸附量为134.8 mg/g。此外，利用机器学习技术来预测树脂的性能，评估五种不同的模型：支持向量回归（SVR）、随机森林、决策树、多层感知器（MLP）和多项式回归。结果表明，该SVR模型的训练R²为0.990，检验R²为0.973，具有最低的平均绝对误差和均方误差。这些发现证明了机器学习在准确模拟吸附过程中的复杂关系方面的有效性，从而为优化废水中重金属的去除提供了有价值的见解。

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引用次数: 0

Structure-property relationship of phosphonium-based polymerized ionic liquids as anion conducting membranes 磷基聚合离子液体作为阴离子导电膜的结构-性能关系

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-25 DOI: 10.1016/j.ceja.2025.101007

Enrica Fontananova , Francesco Galiano , Raffaella Mancuso , Daria Talarico , Gianluca Di Profio , Lorenzo Guazzelli , Christian S. Pomelli , Mario Ferraro , Raffaele Filosa , Vincenzo Formoso , Raffaele G. Agostino , Bartolo Gabriele , Alberto Figoli

Membrane technology in sustainable energy conversion and storage requires the development of tailored membranes able to conjugate high performance (ionic conductivity, perm-selectivity and durability) with acceptable costs and sustainability in their production. In this perspective, polymerizable ionic liquids (PILs) are conductive materials suitable to make high-performing and green ion-conductive membranes combining the unique properties of the ionic liquids, with the advantages of a macromolecular crosslinked polymer. This work presents a deep investigation of the structure-property relationship of phosphonium-based PILs as a functional material for anion-conducting membranes produced by casting and successive photopolymerization (almost solvent-free conditions). The PIL-based membranes prepared were dense, flexible, and completely stable after prolonged contact with water, saline and alkaline solutions. The crosslinking reaction avoided the dissolution of the membrane in water. However, mechanical test highlighted the role of water uptake on mechanical properties of the membranes. Moreover, it was also validated the possibility to blend different PILs in order to combine in synergic way the specific advantages of each component. Electrochemical impedance spectroscopy and membrane potential measurements pointed out a trade-off relationship between the ionic conductivity and perm-selectivity. Moreover, Small Angle X-ray Scattering and differential scanning calorimetry findings shed light on the role of the chemical nature of the PIL on membrane microstructure and transport properties. The main outcome of this research is the possibility to balance the low ionic resistance transport through the charged PILs, with a good stability, tailoring the chemistry of these advanced functional materials.

可持续能量转换和储存的膜技术需要开发出能够结合高性能（离子电导率、热选择性和耐用性）和可接受的成本和生产可持续性的定制膜。从这个角度来看，可聚合离子液体（polymerizable ionic liquid, pil）是一种结合了离子液体的独特性质和大分子交联聚合物的优点，适合制作高性能、绿色离子导电膜的导电材料。本研究深入研究了磷基PILs作为阴离子导电膜的功能材料，通过铸造和连续光聚合（几乎无溶剂条件下）生产。制备的pil基膜在与水、生理盐水和碱性溶液长时间接触后致密、柔韧且完全稳定。交联反应避免了膜在水中的溶解。然而，力学试验强调了吸水对膜力学性能的作用。此外，还验证了混合不同pil的可能性，以便以协同方式结合每种组分的特定优势。电化学阻抗谱和膜电位测量指出了离子电导率和电选择性之间的权衡关系。此外，小角x射线散射和差示扫描量热分析结果揭示了PIL的化学性质对膜微观结构和输运性能的影响。这项研究的主要成果是有可能平衡通过带电pil的低离子电阻传输，具有良好的稳定性，定制这些先进功能材料的化学性质。

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引用次数: 0

Sustainable removal of methylene blue using minimally modified hydrochar from durian peels with experimental adsorption and density functional theory studies 基于实验吸附和密度泛函理论研究的最小改性榴莲果皮碳氢化合物可持续去除亚甲基蓝

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.ceja.2025.101011

Piangjai Peerakiatkhajohn , Praewa Wongburi , Kamonwat Nakason , Bunyarit Panyapinyopol , Khanin Nueangnoraj , Phongphot Sakulaue , Davide Poggio , William Nimmo , Jakkapon Phanthuwongpakdee

This study investigated the use of hydrochar (HC) derived from durian peels as an adsorbent for removing methylene blue (MB) from an aqueous environment. HC was synthesized from durian peels using hydrothermal carbonization under varying temperature (160 – 200 °C) and time (2 – 6 h) conditions. The optimal condition 180 °C for 2 h (HC-180–2) was identified. HC-180–2 was evaluated in MB adsorption experiments and adsorbent characterization. It achieved a maximum MB adsorption capacity (q) of 51.6 mg/g at room temperature, reaching equilibrium within 150 min, and the q value increased to 59.2 mg/g at 65 °C. The adsorption followed pseudo-second-order kinetics (R² = 0.996) and Langmuir isothermal behavior (R² = 0.996), indicating chemisorption on energetically uniform adsorption sites. Thermodynamic analysis yielded Gibbs free energy values ranging from -43.0 to -55.3 kJ/mol and an enthalpy change of 48.5 kJ/mol, which further confirmed the spontaneous and endothermic nature of the chemisorption process. The surface area of HC-180–2 increased from 3.04 to 6.36 m²/g compared to the biomass, confirming the chemisorption and dependence on chemical functionality rather than physical surface area. Structural characterizations revealed enhanced aromatization and functional group formation, including sulfone and ester groups. Density functional theory calculations revealed two possible HC-MB conformation with adsorption mechanisms involving hydrogen bonding, π-π stacking and π-sulfur interactions. The chemisorption nature was also confirmed through Quantum Theory of Atoms in Molecules electron density pathway analysis. While the adsorption capacity was moderate compared to chemically modified adsorbents, the minimally processed durian peels HC positioned itself as a promising green alternative for MB removal.

本研究研究了从榴莲皮中提取的碳氢化合物（HC）作为吸附剂去除水中亚甲基蓝（MB）。以榴莲果皮为原料，在不同温度（160 ~ 200℃）和不同时间（2 ~ 6 h）条件下，采用水热碳化法合成了HC。确定了HC-180-2在180℃下发酵2 h的最佳条件。对HC-180-2进行了吸附实验和吸附性能评价。室温下吸附MB的最大容量(q)为51.6 mg/g， 150 min内达到平衡，65℃时q值增加到59.2 mg/g。吸附符合拟二级动力学（R2 = 0.996）和Langmuir等温行为（R2 = 0.996），表明化学吸附是在能量均匀的吸附位点上进行的。热力学分析得到吉布斯自由能为-43.0 ~ -55.3 kJ/mol，焓变为48.5 kJ/mol，进一步证实了化学吸附过程的自发吸热性质。与生物质相比，HC-180-2的表面积从3.04 m²/g增加到6.36 m²/g，证实了化学吸附作用，并且依赖于化学功能而不是物理表面积。结构表征显示增强的芳构化和官能团形成，包括砜和酯基。密度泛函理论计算揭示了两种可能的HC-MB构象，其吸附机制包括氢键、π-π堆积和π-硫相互作用。通过分子原子量子理论的电子密度路径分析也证实了化学吸附的性质。虽然与化学改性吸附剂相比，其吸附能力适中，但经过最小加工的榴莲皮HC将自己定位为一种有前途的绿色MB去除替代品。

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引用次数: 0

Strategies of designing advanced dual-functional metal-organic frameworks for sustainable wastewater treatment applications 设计先进的双功能金属-有机框架的可持续废水处理应用策略

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2026-01-08 DOI: 10.1016/j.ceja.2026.101040

Pasha W. Sayyad , Hassan Gomaa , Rana Sabouni

Metal-organic frameworks (MOFs) have emerged as promising materials for advanced wastewater treatment owing to their tunable structures and versatile functionalities. A myriad of studies has explored their role in detection, adsorption, and photocatalytic degradation. However, designing MOF-based composites capable of integrating dual functions within one framework remains a considerable challenge. As a result, and despite of their potential, dual-functional MOF systems are still relatively scarce and developing strategies to enable detection and removal/degradation of pollutants with single framework for environment applications has become the focus of many researchers. This review highlights cutting-edge strategies that advance the development of multifunctional MOF-based composites for wastewater treatment, along with future perspectives for their sustainable utilization, current challenges and future research directions. We believe this review would inspire researchers to explore emerging strategies to develop multifunctional MOF for advancing water treatment technologies toward next generation of sustainable environment applications.

金属有机骨架（mof）因其结构可调和功能多样而成为污水深度处理的重要材料。无数的研究已经探索了它们在检测、吸附和光催化降解中的作用。然而，设计能够在一个框架内集成双重功能的基于mof的复合材料仍然是一个相当大的挑战。因此，尽管具有潜力，但双功能MOF系统仍然相对稀缺，开发能够在单一框架下检测和去除/降解污染物的环境应用策略已成为许多研究人员关注的焦点。本文综述了用于污水处理的多功能mof基复合材料的发展前沿策略，以及其可持续利用的未来前景，当前的挑战和未来的研究方向。我们相信这一综述将激励研究人员探索开发多功能MOF的新兴策略，以推进水处理技术向下一代可持续环境应用。

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引用次数: 0

Green conversion of potato-based starchy waste into photocatalyst coupled nanoparticles for efficient removal of reactive red 195 dye from textile effluents 马铃薯基淀粉废物转化为光催化剂偶联纳米颗粒的绿色转化，以有效去除纺织废水中的活性红195染料

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-24 DOI: 10.1016/j.ceja.2025.101013

M.M. Nour , Maha A. Tony , Mai K. Fouad , Hossam A. Nabwey

A sustainable and low-cost route is presented for converting potato-based starchy waste into biochar–magnetite (P-Char@Fe₃O₄) composites that function as efficient heterogeneous photo-Fenton catalysts for the degradation of the azo dye Reactive Red 195 (RR195) in textile effluents. Potato peel biochars pyrolyzed at 200–600 °C were coupled with Fe₃O₄ nanoparticles that is prepared by co-precipitation to produce P-Char200@Fe₃O₄, P-Char400@Fe₃O₄, and P-Char600@Fe₃O₄. SEM/EDS and elemental mapping confirmed the successful anchoring of Fe₃O₄ and revealed temperature-dependent dispersion and crystallinity across the carbon matrix. Catalytic screening showed a performance trend of P-Char200@Fe₃O₄ > P-Char400@Fe₃O₄ > P-Char600@Fe₃O₄, attributed to the preservation of oxygenated surface functionalities and accessible pore structures at lower/intermediate pyrolysis temperatures. Optimized operation with the robust P-Char400@Fe₃O₄ (pH 6.5, catalyst 40 mg L⁻¹, H₂O₂ 400 mg L⁻¹, UV irradiation) achieved nearly 100% RR195 removal within 20 min. The system remained tolerant to realistic conditions, showing enhanced performance with increasing temperature (32–60 °C) but declining efficiency at high dye loads or excessive H₂O₂. Kinetic analysis confirmed pseudo-first-order behavior (R² > 0.98), while Arrhenius/Eyring evaluation yielded an activation energy of 30.98 kJ mol⁻¹, positive enthalpy of activation, and negative entropy that consistent with a surface-organized, radical-mediated mechanism. The catalyst preserved about 80% efficiency after six reuse cycles, demonstrating strong magnetic recoverability and structural stability. Compared with conventional Fenton and modified systems, the agro-waste-derived P-Char@Fe₃O₄ enables rapid decolorization at near-neutral pH, reduces sludge generation, and advances circular-economy valorization of food-processing residues, highlighting its potential for scalable textile wastewater treatment.

提出了一种可持续、低成本的方法，将马铃薯基淀粉废物转化为生物炭-磁铁矿（P-Char@Fe₃O₄）复合材料，该复合材料作为高效的非均相光- fenton催化剂，用于降解纺织废水中的偶氮染料活性红195 （RR195）。将200-600℃热解的马铃薯皮生物炭与共沉淀法制备的Fe₃O₄纳米颗粒偶联，得到P-Char200@Fe₃O₄、P-Char400@Fe₃O₄和P-Char600@Fe₃O₄。SEM/EDS和元素映射证实了Fe₃O₄的成功锚定，并揭示了碳基体上的温度依赖性分散和结晶度。催化筛选结果表明：P-Char200@Fe₃O₄>； P-Char400@Fe₃O₄>； P-Char600@Fe₃O₄在低/中热解温度下保留了氧合表面官能和可达的孔结构。优化后的操作采用稳健的P-Char400@Fe₃O₄（pH 6.5，催化剂40mg L⁻¹，H₂O₂400mg L⁻¹，紫外线照射），在20分钟内几乎100%去除了RR195。该系统在实际条件下仍然具有耐受性，随着温度的升高（32-60°C）表现出增强的性能，但在高染料负载或过多的h2o₂下效率下降。动力学分析证实了伪一阶行为（R²> 0.98），而Arrhenius/Eyring评价得出活化能为30.98 kJ mol⁻(1)，激活焓为正，熵为负，符合表面组织的自由基介导机制。经过6次重复使用，催化剂的效率保持在80%左右，表现出较强的磁可恢复性和结构稳定性。与传统的Fenton和改性系统相比，由农业废弃物衍生的P-Char@Fe₃O₄能够在接近中性的pH值下快速脱色，减少污泥的产生，并推进食品加工残留物的循环经济价值，突出了其在可扩展的纺织废水处理方面的潜力。

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引用次数: 0

Demonstration of H2/CO2 acetogenesis by Acetobacterium wieringae in a trickle-bed reactor without organic substrates wieringae醋酸杆菌在无有机底物的滴床反应器中氢气/二氧化碳丙酮生成的演示

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ceja.2025.101029

Paola Andrea Palacios , Michael Vedel Wegener Kofoed

CO₂-to-acetate applications via microbial H₂/CO₂ gas fermentation were investigated in a 2.2 L trickle-bed reactor (TBR) using Acetobacterium wieringae as a mesophilic monoculture. The fermentation was carried out at a continuous gas flow rate of 25 NmL min⁻¹ in a minimal medium without any organic substrates, including yeast extract. Monitoring of gas consumption, product quantification, pH, pressure, OD₆₀₀, and optical microscopy revealed that the TBR produced 18.4 g L⁻¹ of acetate as the sole end organic acid at a constant pH and OD₆₀₀ of 0.2. This study highlights the ability of A. wieringae to form biofilm and sustain long-term stability for acetate production, as the fermentation was carried over 49 days. However, quantitative analyses showed that the largest part of the biomass was present in the liquid layer surrounding the biocarriers. Further optimization is required before scaling up, including reducing gas flow rates and employing carrier materials that enhance cell attachment and biofilm formation. This work provides a foundation for developing more sustainable acetate production systems using minimal resources, renewable gases, and efficient biocatalysts.

在2.2 L滴流床反应器（TBR）上，以嗜温嗜酸醋酸杆菌（Acetobacterium wieringae）为单培养菌，研究了微生物H2/CO2气体发酵对CO2-乙酸的应用。在最小的培养基中以25 NmL min的连续气体流速进行发酵，没有任何有机底物，包括酵母提取物。对燃气消耗量、产品定量、pH值、压力、OD₆₀、光学显微镜的监测表明，在pH值恒定、OD₆₀为0.2的情况下，TBR生产的唯一末端有机酸为18.4 g L - 1醋酸酯。本研究强调了A. wieringae在发酵超过49天的情况下形成生物膜并维持醋酸盐生产的长期稳定性的能力。然而，定量分析表明，大部分生物量存在于生物载体周围的液体层中。在扩大规模之前，还需要进一步优化，包括降低气体流速和使用增强细胞附着和生物膜形成的载体材料。这项工作为开发更可持续的醋酸酯生产系统提供了基础，该系统使用最少的资源、可再生气体和高效的生物催化剂。

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引用次数: 0

A deep reinforcement learning framework for regime-specific decision in the operation of industrial ammonium sulfate crystallization 工业硫酸铵结晶操作中特定制度决策的深度强化学习框架

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2026-01-20 DOI: 10.1016/j.ceja.2026.101051

Thanchanok Archapitakkol , Santi Bardeeniz , Thanayuth Kaweetirawatt , Chanin Panjapornpon

Real-time prediction of crystal size in industrial ammonium sulfate (AS) crystallization remains a challenge due to opaque slurry conditions, delayed laboratory measurements, variability across nucleation and crystal growth, and a mismatched sampling interval between inputs and output. Therefore, this study develops a reinforcement-learning router for regime-specific decision-making to guide a multi-model prediction framework in estimating the ratio of large AS crystals. The operating domain is segmented into distinct regimes, and a specialized long short-term memory is trained for each segmented regime to learn the characteristics and local dynamics. A Deep Q-Network (DQN) router evaluates sequential inputs to make a decision and select the best submodel by balancing predictive accuracy and operational consistency. The model is verified on the industrial-scale crystallization system. Based on the results, the DQN-router agent demonstrates effective performance across the distinct operatiing regimes using ten actions. The proposed model achieves a coefficient of determination of 0.961, a root mean square error of 1.810, and a mean absolute percentage error of 1.392 % and outperforms a single-prediction model without decision-making and benchmarked selectors. Analysis of router decisions confirms that the learned policy adapts effectively with shifting operating conditions and resolves overlaps between regime boundaries. A constrained optimization analysis of the model predictions identifies an optimal 19-hour operating pattern, consisting of an 11-hour hold-up period and an 8-hour discharge period, which produces an average large-crystal ratio of 88.38 % ± 2.09 %.

由于浆液条件不透明、实验室测量延迟、成核和晶体生长的可变性以及输入和输出之间的采样间隔不匹配，工业硫酸铵（AS）结晶晶体尺寸的实时预测仍然是一个挑战。因此，本研究开发了一种用于特定状态决策的强化学习路由，以指导多模型预测框架估计大AS晶体的比例。将操作域划分为不同的区域，并针对每个区域训练专门的长短期记忆来学习特征和局部动态。深度Q-Network （Deep Q-Network， DQN）路由器对序列输入进行评估，通过平衡预测精度和操作一致性来做出决策并选择最佳子模型。该模型在工业规模结晶系统上得到了验证。基于结果，DQN-router代理使用十个动作在不同的操作制度中展示了有效的性能。该模型的决定系数为0.961，均方根误差为1.810，平均绝对百分比误差为1.392%，优于没有决策和基准选择器的单一预测模型。对路由器决策的分析证实，学习到的策略能够有效地适应不断变化的运行条件，并解决了状态边界之间的重叠。对模型预测进行约束优化分析，确定了最佳的19小时工作模式，包括11小时的保持时间和8小时的放电时间，平均大晶比为88.38%±2.09%。

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引用次数: 0

Energy optimization in electrochemical oxidation for wastewater treatment using interpretable machine learning 利用可解释机器学习进行废水处理的电化学氧化能量优化

IF 7.1 Q1 ENGINEERING, CHEMICAL

Chemical Engineering Journal Advances

Pub Date : 2026-03-01 Epub Date: 2026-01-13 DOI: 10.1016/j.ceja.2026.101044

Mohammad Baghani, Mahdieh Soltanian, Mohammad-Hossein Sarrafzadeh

Electrochemical oxidation (EO) promises broad-spectrum removal of recalcitrant pollutants from wastewater but is often penalized by high energy demand and uncertain scale-up. This study presents a data-driven framework that links EO operating conditions to energy consumption and removal performance using a curated dataset of 255 experimental conditions collected from the literature and laboratory reports. Three regression models, Random Forest, SVR and XGBoost were trained, compared, and tuned through nested cross-validation, with XGBoost yielding the best predictive accuracy (5-fold CV R² = 0.9727, RMSE = 25.7 kWh·m⁻³, MAE = 8.95 kWh·m⁻³). Interpretability analyses (feature gain, SHAP summary, PDPs and ablation tests) show that current density and pollutant class jointly explain the majority of variance, with current density alone accounting for the largest single share. A leave-one-study-out experiment reveals that prediction degrades when extrapolating to pollutant categories not represented in the training set, highlighting a clear limitation for broad extrapolation. The complete dataset, trained models and reproducible code are provided in the supplementary repository, supporting transport and data-driven optimization of EO systems for wastewater treatment.

电化学氧化（EO）有望广谱去除废水中的顽固性污染物，但往往受到高能耗和不确定规模的限制。本研究提出了一个数据驱动的框架，使用从文献和实验室报告中收集的255个实验条件的精心整理的数据集，将EO操作条件与能耗和去除性能联系起来。对随机森林、SVR和XGBoost三种回归模型进行了训练、比较和调整，通过嵌套交叉验证，XGBoost的预测精度最高（5倍CV R²= 0.9727,RMSE = 25.7 kWh·m⁻³，MAE = 8.95 kWh·m⁻³）。可解释性分析（特征增益、SHAP摘要、pdp和消融测试）表明，电流密度和污染物类别共同解释了大部分差异，其中电流密度单独占最大的份额。一个留一项研究的实验表明，当外推到未在训练集中表示的污染物类别时，预测会下降，这突出了广泛外推的明显局限性。在补充存储库中提供了完整的数据集，训练模型和可重复的代码，支持废水处理EO系统的传输和数据驱动优化。

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引用次数: 0