ACS Engineering Au最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":4.3,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/egv005i003_1948644","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144429477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Reinforcement Learning-Based Self-Optimization of Flow Chemistry","authors":"Ashish Yewale,&nbsp;Yihui Yang,&nbsp;Neda Nazemifard,&nbsp;Charles D. Papageorgiou,&nbsp;Chris D. Rielly and Brahim Benyahia*,&nbsp;","doi":"10.1021/acsengineeringau.5c0000410.1021/acsengineeringau.5c00004","DOIUrl":"https://doi.org/10.1021/acsengineeringau.5c00004https://doi.org/10.1021/acsengineeringau.5c00004","url":null,"abstract":"<p >The development of effective synthetic pathways is critical in many industrial sectors. The growing adoption of flow chemistry has opened new opportunities for more cost-effective and environmentally friendly manufacturing technologies. However, the development of effective flow chemistry processes is still hampered by labor- and experiment-intensive methodologies and poor or suboptimal performance. In this context, integrating advanced machine learning strategies into chemical process optimization can significantly reduce experimental burdens and enhance overall efficiency. This paper demonstrates the capabilities of deep reinforcement learning (DRL) as an effective self-optimization strategy for imine synthesis in flow, a key building block in many compounds such as pharmaceuticals and heterocyclic products. A deep deterministic policy gradient (DDPG) agent was designed to iteratively interact with the environment, the flow reactor, and learn how to deliver optimal operating conditions. A mathematical model of the reactor was developed based on new experimental data to train the agent and evaluate alternative self-optimization strategies. To optimize the DDPG agent’s training performance, different hyperparameter tuning methods were investigated and compared, including trial-and-error and Bayesian optimization. Most importantly, a novel adaptive dynamic hyperparameter tuning was implemented to further enhance the training performance and optimization outcome of the agent. The performance of the proposed DRL strategy was compared against state-of-the-art gradient-free methods, namely SnobFit and Nelder–Mead. Finally, the outcomes of the different self-optimization strategies were tested experimentally. It was shown that the proposed DDPG agent has superior performance compared to its self-optimization counterparts. It offered better tracking of the global solution and reduced the number of required experiments by approximately 50 and 75% compared to Nelder–Mead and SnobFit, respectively. These findings hold significant promise for the chemical engineering community, offering a robust, efficient, and sustainable approach to optimizing flow chemistry processes and paving the way for broader integration of data-driven methods in process design and operation.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"247–266 247–266"},"PeriodicalIF":4.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.5c00004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning-Assisted Recovery of Delicate Kinetic Information from Transient Reactor Experiments","authors":"Shengguang Wang*,&nbsp;Han Chau,&nbsp;Stephen Kristy,&nbsp;Brooklyne Ariana Thompson,&nbsp;Jason P. Malizia and Rebecca Fushimi*,&nbsp;","doi":"10.1021/acsengineeringau.5c0002510.1021/acsengineeringau.5c00025","DOIUrl":"https://doi.org/10.1021/acsengineeringau.5c00025https://doi.org/10.1021/acsengineeringau.5c00025","url":null,"abstract":"<p >Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al₂O₃ and Pt/SiO₂ catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al₂O₃ catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO₂ catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO₂. We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"298–310 298–310"},"PeriodicalIF":4.3,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.5c00025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Synthesis and Characterization of Dy2O3@TiO2 Nanocomposites for Enhanced Photocatalytic and Electrocatalytic Applications”","authors":"Balachandran Subramanian*,&nbsp;K. Jeeva Jothi,&nbsp;Mohamedazeem M. Mohideen,&nbsp;R. Karthikeyan,&nbsp;A. Santhana Krishna Kumar*,&nbsp;Ganeshraja Ayyakannu Sundaram,&nbsp;K. Thirumalai,&nbsp;Munirah D. Albaqami,&nbsp;Saikh Mohammad and M. Swaminathan*,&nbsp;","doi":"10.1021/acsengineeringau.5c0001510.1021/acsengineeringau.5c00015","DOIUrl":"https://doi.org/10.1021/acsengineeringau.5c00015https://doi.org/10.1021/acsengineeringau.5c00015","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"311 311"},"PeriodicalIF":4.3,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.5c00015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anchoring Computational Flow Models to Real-World Multiphase Reactors: Toward Ensuring Delivery of Materials and Energy at the Right Time and Place in Reactors","authors":"Vivek V. Ranade*,&nbsp;","doi":"10.1021/acsengineeringau.4c0006210.1021/acsengineeringau.4c00062","DOIUrl":"https://doi.org/10.1021/acsengineeringau.4c00062https://doi.org/10.1021/acsengineeringau.4c00062","url":null,"abstract":"<p >Multiphase reactors (MPRs) are crucial in converting raw materials into essential products such as chemicals, polymers, and medicines and contribute immensely to the global economy. MPRs are complex dynamical systems involving chemical reactions and interphase transport processes. State-of-the-art designs of MPRs often struggle to deliver materials and energy precisely at the right time and place in the reactor, leading to unwanted side products and excess energy consumption. This is mainly due to our inability to accurately predict and direct the flow of materials and energy within MPRs. In this Perspective, I propose a novel way of developing high-fidelity models of MPRs by synergistically combining wall pressure fluctuation data acquired from these MPRs with machine learning and physics-based models. This novel approach has the potential to capture multiscale information contained in pressure fluctuations and thereby deliver unprecedented accuracy to MPR models. This will enhance their fidelity and applicability to real-world reactors without needing resolution of micro- and mesoscales or using any ad hoc adjustments. The novel methodology is discussed by considering a case of bubble column reactor as a representative MPR. Evidence available in the published studies that lends support to the key hypothesis underlying the proposed methodology is briefly discussed. Specific suggestions on how to develop and validate the proposed approach are included. The proposed approach will lead to high-fidelity models anchored to real-world reactors via wall pressure fluctuations and thereby facilitate the identification and implementation of optimal strategic interventions to influence the multiphase transport in MPRs. This will ensure precise delivery of materials and energy and thereby eliminate side products and minimize energy consumption. I believe that it will transform the foundations of simulating and intensifying MPRs, leading to significantly better resource utilization and reduced emissions in the future.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"183–190 183–190"},"PeriodicalIF":4.3,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.4c00062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent Advances in Photocatalytic Conversion of Lignocellulosic Biomass: Routes, Limitations, and Outlook","authors":"Kavitha S,&nbsp;Yukesh Kannah Ravi,&nbsp;Ginni G,&nbsp;Lise Appels,&nbsp;Mieczysław Łapkowski,&nbsp;Yogendra Kumar Mishra,&nbsp;Palanivelu Kandasamy,&nbsp;Palanichamy Rajaguru,&nbsp;Pugalenthi Velan and Rajesh Banu Jeyakumar*,&nbsp;","doi":"10.1021/acsengineeringau.4c0005910.1021/acsengineeringau.4c00059","DOIUrl":"https://doi.org/10.1021/acsengineeringau.4c00059https://doi.org/10.1021/acsengineeringau.4c00059","url":null,"abstract":"<p >Lignocellulosic biomass (LCB) is an abundant resource for recovering fuels and value-added products. Despite extensive investigations and research, the complete unlocking of LCB potency has yet to be accomplished. The photocatalytic conversion of LCB, which utilizes renewable solar light under mild conditions, has been recognized as the hottest current research topic receiving attention for sustainable development. Numerous technical challenges have been identified for effective practical implementations. In brief, photocatalytic conversion oxidizes C_β─O/C_α─C_β linkages in LCB to recover fuels and biochemicals. From a chemical viewpoint, optimizing the exclusive interaction of oxidizing radical groups and radical intermediates through suitable regulation of their type and recovery is crucial for selectively generating desirable products. This review provides recent insights into the mechanistic pathways of the selective conversion of LCB via reactive oxygen species (ROS) behavior optimization and system design. In addition, this review highlights the up-to-date achievements in the photocatalysis of LCB and its components as well as the selective oxidation of the prominent linkages of lignin, native biomass valorization, cellulose, hemicellulose, and its derivatives. Further, upgrading of bioplatforms and electricity generation via LCB photocatalysis is discussed in detail as a novel approach. The prospects and opportunities of using LCB photocatalysis to improve the viability of photocatalytic conversion of LCB are also discussed.</p>","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 3","pages":"191–225 191–225"},"PeriodicalIF":4.3,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsengineeringau.4c00059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144305676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Owen Watts Moore,&nbsp;Thomas Andrew Waigh*,&nbsp;Ali Arafeh,&nbsp;Philip Martin,&nbsp;Cesar Mendoza and Adam Kowalski,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 2","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":4.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsengineeringau.4c00043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144416036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Paul D. Goring,&nbsp;Amelia Newman,&nbsp;Christopher W. Jones* and Shelley D. Minteer*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 2","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":4.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsengineeringau.5c00013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144416026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 2","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":4.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/egv005i002_1924557","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144416043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"Claudio Ferroni,&nbsp;Mauro Bracconi,&nbsp;Matteo Ambrosetti,&nbsp;Gianpiero Groppi,&nbsp;Matteo Maestri and Enrico Tronconi*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":29804,"journal":{"name":"ACS Engineering Au","volume":"5 2","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":4.3,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsengineeringau.4c00057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144346871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}