Computers & Chemical Engineering最新文献

英文中文

Knowledge-enhanced data-driven modeling of wastewater treatment processes for energy consumption prediction

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-17 DOI: 10.1016/j.compchemeng.2024.108982

Louis Allen, Joan Cordiner

Rising energy usage in wastewater treatment processes (WWTPs) poses pressing economic and environmental challenges. Machine learning approaches to model these complex systems have been limited by highly non-linear processes and high dataset noise. To address this, we introduce a novel Knowledge-Enhanced Graph Disentanglement framework for Energy Consumption Prediction (KEGD-EC) that leverages causal inference and graph neural networks. This work combines specific knowledge of causal relationships with a disentangled graph convolutional network architecture to facilitate accurate predictions. In a study on a WWTP in Melbourne, we demonstrate a 59.7% reduction in root mean squared error in energy consumption prediction using KEGD-EC compared to the next best model. We show that causal models built using domain knowledge outperform data-driven causal discovery models for complex systems. These results signify a step forward in applying machine learning to complex manufacturing processes, with the integration of causal knowledge into deep learning architectures posing a promising area of research for predictive analytics in manufacturing.

{"title":"Knowledge-enhanced data-driven modeling of wastewater treatment processes for energy consumption prediction","authors":"Louis Allen, Joan Cordiner","doi":"10.1016/j.compchemeng.2024.108982","DOIUrl":"10.1016/j.compchemeng.2024.108982","url":null,"abstract":"<div><div>Rising energy usage in wastewater treatment processes (WWTPs) poses pressing economic and environmental challenges. Machine learning approaches to model these complex systems have been limited by highly non-linear processes and high dataset noise. To address this, we introduce a novel Knowledge-Enhanced Graph Disentanglement framework for Energy Consumption Prediction (KEGD-EC) that leverages causal inference and graph neural networks. This work combines specific knowledge of causal relationships with a disentangled graph convolutional network architecture to facilitate accurate predictions. In a study on a WWTP in Melbourne, we demonstrate a 59.7% reduction in root mean squared error in energy consumption prediction using KEGD-EC compared to the next best model. We show that causal models built using domain knowledge outperform data-driven causal discovery models for complex systems. These results signify a step forward in applying machine learning to complex manufacturing processes, with the integration of causal knowledge into deep learning architectures posing a promising area of research for predictive analytics in manufacturing.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108982"},"PeriodicalIF":3.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model predictive control of purple bacteria in raceway reactors: Handling microbial competition, disturbances, and performance

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-17 DOI: 10.1016/j.compchemeng.2024.108981

Ali Moradvandi , Bart De Schutter , Edo Abraham , Ralph E.F. Lindeboom

Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer a more cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized Model Predictive Control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria Model (PBM) simulates this process, and simulation results demonstrate the control system’s effectiveness and robustness.

{"title":"Model predictive control of purple bacteria in raceway reactors: Handling microbial competition, disturbances, and performance","authors":"Ali Moradvandi , Bart De Schutter , Edo Abraham , Ralph E.F. Lindeboom","doi":"10.1016/j.compchemeng.2024.108981","DOIUrl":"10.1016/j.compchemeng.2024.108981","url":null,"abstract":"<div><div>Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer a more cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized Model Predictive Control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria Model (PBM) simulates this process, and simulation results demonstrate the control system’s effectiveness and robustness.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108981"},"PeriodicalIF":3.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A risk-based maintenance planning in process industry using a bi-objective robust optimization model

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-16 DOI: 10.1016/j.compchemeng.2024.108984

Zohreh Alipour , Mohammadali Saniee Monfared , Sayyed Ehsan Monabbati

We have developed an innovative risk-based maintenance planning methodology using a bi-objective scenario-based robust optimization model. This approach determines robust, optimal maintenance intervals for process industries. Our methodology comprises two main phases: risk assessment and maintenance planning. In the initial phase, we identified critical items using a Bow-tie diagram, which is subsequently mapped into a Bayesian network to estimate the overall risk based on historical data and process knowledge. In the second phase, we developed a bi-objective scenario-based robust optimization model to Pareto-optimize both risk and cost under operational risks. This results in a robust maintenance plan capable of withstanding time, costs, and failure rate uncertainties inherent in process industries with considering decision-makers' attitudes to risk (risk-averse, risk-neutral, or hybrid attitude). The computational results demonstrate the significant impact of considering uncertainty of critical data, and robustness on the selected maintenance plan and system performance.

{"title":"A risk-based maintenance planning in process industry using a bi-objective robust optimization model","authors":"Zohreh Alipour , Mohammadali Saniee Monfared , Sayyed Ehsan Monabbati","doi":"10.1016/j.compchemeng.2024.108984","DOIUrl":"10.1016/j.compchemeng.2024.108984","url":null,"abstract":"<div><div>We have developed an innovative risk-based maintenance planning methodology using a bi-objective scenario-based robust optimization model. This approach determines robust, optimal maintenance intervals for process industries. Our methodology comprises two main phases: risk assessment and maintenance planning. In the initial phase, we identified critical items using a Bow-tie diagram, which is subsequently mapped into a Bayesian network to estimate the overall risk based on historical data and process knowledge. In the second phase, we developed a bi-objective scenario-based robust optimization model to Pareto-optimize both risk and cost under operational risks. This results in a robust maintenance plan capable of withstanding time, costs, and failure rate uncertainties inherent in process industries with considering decision-makers' attitudes to risk (risk-averse, risk-neutral, or hybrid attitude). The computational results demonstrate the significant impact of considering uncertainty of critical data, and robustness on the selected maintenance plan and system performance.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108984"},"PeriodicalIF":3.9,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart Process Analytics for Process Monitoring

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-14 DOI: 10.1016/j.compchemeng.2024.108918

Fabian Mohr , Elia Arnese-Feffin , Massimliano Barolo , Richard D. Braatz

Process monitoring is critical to ensuring product quality and efficient, safe process operation. Data-driven modeling is used in the process industries to build fault detection systems. No single data-driven modeling method provides the best fault detection performance for all process systems, and the selection of the best data-driven modeling method for a specific process system requires substantial expertise. In this study, we propose Smart Process Analytics for Process Monitoring (SPAfPM), a systematic framework for automatic method selection and calibration of data-driven fault detection models. A set of candidate methods is pre-selected from a library on the basis of the characteristics of the data. A rigorous cross-validation procedure is then employed to compare the models obtained by these methods to select the best data-driven model for fault detection. The performance of SPAfPM is demonstrated in four case studies, including the Tennessee Eastman Process.

引用次数: 0

Phase stability criteria and fluid-phase equilibria in strong-electrolyte systems

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-13 DOI: 10.1016/j.compchemeng.2024.108977

Felipe A. Perdomo , George Jackson , Alexander Mitsos , Amparo Galindo , Claire S. Adjiman

Although the presence of salts can significantly affect the fluid-phase equilibria, phase stability and equilibrium calculations remain challenging due to the nonlinearity of thermodynamic models and to the negligible amounts of ions that can be present in some phases. To address this, we introduce a new variable transformation and present the first formal proof of a stability criterion for strong (fully-dissociated) electrolyte solutions based on the tangent-plane distance under the electroneutrality constraint. The criterion can also be recast based on duality theory, yielding two alternative formulations with/without reformulation in the volume-composition space. We use these theoretical results to extend the Helmholtz free Energy Lagrangian Dual (HELD) algorithm (Pereira et al., Comput. Chem. Eng. 36 (2012) 99) to strong electrolyte mixtures. The resulting HELD2.0 algorithm provides reliable calculations of the nonideal phase behaviour of mixtures of organic molecules and water with alkali halide salts for a wide range of thermodynamic states.

{"title":"Phase stability criteria and fluid-phase equilibria in strong-electrolyte systems","authors":"Felipe A. Perdomo , George Jackson , Alexander Mitsos , Amparo Galindo , Claire S. Adjiman","doi":"10.1016/j.compchemeng.2024.108977","DOIUrl":"10.1016/j.compchemeng.2024.108977","url":null,"abstract":"<div><div>Although the presence of salts can significantly affect the fluid-phase equilibria, phase stability and equilibrium calculations remain challenging due to the nonlinearity of thermodynamic models and to the negligible amounts of ions that can be present in some phases. To address this, we introduce a new variable transformation and present the first formal proof of a stability criterion for strong (fully-dissociated) electrolyte solutions based on the tangent-plane distance under the electroneutrality constraint. The criterion can also be recast based on duality theory, yielding two alternative formulations with/without reformulation in the volume-composition space. We use these theoretical results to extend the Helmholtz free Energy Lagrangian Dual (HELD) algorithm (Pereira et al., Comput. Chem. Eng. 36 (2012) 99) to strong electrolyte mixtures. The resulting HELD2.0 algorithm provides reliable calculations of the nonideal phase behaviour of mixtures of organic molecules and water with alkali halide salts for a wide range of thermodynamic states.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108977"},"PeriodicalIF":3.9,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Retraction notice to “Economic, reliability, environmental and operation factors to achieve optimal operations of multiple microgrids” [Computers & Chemical Engineering 176 (2023) 108279]

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-12 DOI: 10.1016/j.compchemeng.2024.108971

Aiqin Xu , Jing Wu , Guoliang Zhou , Sara Saeedi

引用次数: 0

Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-12 DOI: 10.1016/j.compchemeng.2024.108975

Quirin Göttl , Jonathan Pirnay , Jakob Burger , Dominik G. Grimm

Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years. Existing work on reinforcement learning for flowsheet synthesis shows promising concepts. We further develop those concepts and present a general deep reinforcement learning approach for flowsheet synthesis. We demonstrate the adaptability of an agent to the general task of separating binary azeotropic mixtures. The agent is trained to set up the discrete process topology alongside choosing continuous specifications for the individual flowsheet elements (e.g., distillation columns and recycles). Without prior knowledge, it learns within one training cycle to craft flowsheets for multiple chemical systems, considering different feed compositions and conceptual approaches. The agent discovers autonomously fundamental process engineering paradigms as heteroazeotropic distillation or curved-boundary distillation.

{"title":"Deep reinforcement learning enables conceptual design of processes for separating azeotropic mixtures without prior knowledge","authors":"Quirin Göttl , Jonathan Pirnay , Jakob Burger , Dominik G. Grimm","doi":"10.1016/j.compchemeng.2024.108975","DOIUrl":"10.1016/j.compchemeng.2024.108975","url":null,"abstract":"<div><div>Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years. Existing work on reinforcement learning for flowsheet synthesis shows promising concepts. We further develop those concepts and present a general deep reinforcement learning approach for flowsheet synthesis. We demonstrate the adaptability of an agent to the general task of separating binary azeotropic mixtures. The agent is trained to set up the discrete process topology alongside choosing continuous specifications for the individual flowsheet elements (e.g., distillation columns and recycles). Without prior knowledge, it learns within one training cycle to craft flowsheets for multiple chemical systems, considering different feed compositions and conceptual approaches. The agent discovers autonomously fundamental process engineering paradigms as heteroazeotropic distillation or curved-boundary distillation.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108975"},"PeriodicalIF":3.9,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Superstructure optimization with rigorous models via an exact reformulation

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-12 DOI: 10.1016/j.compchemeng.2024.108972

Smitha Gopinath , Claire S. Adjiman

The applicability of superstructure optimization to process synthesis is often limited to simple models and flowsheets. The state operator network (SON) (Smith and Pantelides, 1995) overcomes some limitations via a mixer–splitter network, allowing the use of rigorous unit models. However, setting flowrates to zero for non-selected units can result in numerical issues. Here, the modified state operator network (MSON), a new exact reformulation with modified mixers and splitters, is introduced. When a unit is deselected, a fictitious, strictly positive, mixer inlet flow ensures the unit model is easily solved. A corresponding fictitious splitter outlet counteracts this inlet, resulting in correct flowsheet behaviour. When applied to a toy flowsheet, the MSON outperforms standard formulations. When applied to the synthesis of a reactor–separator network and to a challenging counter-current column synthesis problem, the MSON offers a systematic and robust approach to superstructure optimization.

引用次数: 0

Simple control structure for stabilizing Core Annular Flow operation in heavy oil transportation

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-12 DOI: 10.1016/j.compchemeng.2024.108978

Patrick Lima , Erbet Costa , Teófilo Paiva Guimarães Mendes , Leizer Schnitman , Sigurd Skogestad , Idelfonso Nogueira

This research aims to develop a simple regulatory controller to control a Core Annular Flow (CAF) in the oil and gas industry, focusing on transporting heavy oils. CAF is an economical method to transport viscous crude oil where less viscous liquid, typically water, is used to lubricate the pipe walls, creating an annular flow regime. However, managing the stability of CAF is challenging due to geometric variations, changes in pipeline flow direction, and emulsion formation. We used computational fluid dynamics (CFD) simulations to represent the CAF system and subsequently designed a simple control structure for the process. This process involved conducting both open-loop and closed-loop tests. The findings from the study indicate that the I controller significantly improves the system's response to disturbances in oil velocity by adeptly adjusting water velocity. This adjustment is crucial for maintaining the desired oil fraction and sustaining an annular flow pattern. An important observation was the effectiveness of the proportional gain in tracking the setpoint within annular flow regimes and the enhanced system stability achieved by increasing the integral action. The study concludes that the PI controller stabilizes operations in previously challenging conditions and expands the system's operational range.

{"title":"Simple control structure for stabilizing Core Annular Flow operation in heavy oil transportation","authors":"Patrick Lima , Erbet Costa , Teófilo Paiva Guimarães Mendes , Leizer Schnitman , Sigurd Skogestad , Idelfonso Nogueira","doi":"10.1016/j.compchemeng.2024.108978","DOIUrl":"10.1016/j.compchemeng.2024.108978","url":null,"abstract":"<div><div>This research aims to develop a simple regulatory controller to control a Core Annular Flow (CAF) in the oil and gas industry, focusing on transporting heavy oils. CAF is an economical method to transport viscous crude oil where less viscous liquid, typically water, is used to lubricate the pipe walls, creating an annular flow regime. However, managing the stability of CAF is challenging due to geometric variations, changes in pipeline flow direction, and emulsion formation. We used computational fluid dynamics (CFD) simulations to represent the CAF system and subsequently designed a simple control structure for the process. This process involved conducting both open-loop and closed-loop tests. The findings from the study indicate that the I controller significantly improves the system's response to disturbances in oil velocity by adeptly adjusting water velocity. This adjustment is crucial for maintaining the desired oil fraction and sustaining an annular flow pattern. An important observation was the effectiveness of the proportional gain in tracking the setpoint within annular flow regimes and the enhanced system stability achieved by increasing the integral action. The study concludes that the PI controller stabilizes operations in previously challenging conditions and expands the system's operational range.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"194 ","pages":"Article 108978"},"PeriodicalIF":3.9,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143136625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multistage robust mixed-integer optimization for industrial demand response with interruptible load

IF 3.9 2区工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers & Chemical Engineering

Pub Date : 2024-12-11 DOI: 10.1016/j.compchemeng.2024.108974

Jnana Sai Jagana , Satyajith Amaran , Qi Zhang

Industrial demand response is an effective strategy for power-intensive manufacturing plants to reduce operating costs and in turn also contribute to the reliable operation of the power grid. Although industrial processes are increasingly participating in various demand response activities, the financially incentivized provision of interruptible load is still not well explored. This could be due to the uncertainty that, when providing interruptible load, one does not know in advance when load reduction will be requested. We apply an adjustable robust optimization approach to address this uncertainty in the production schedule of a continuous industrial process providing interruptible load. Piecewise linear decision rules, which can allow for both continuous and discrete recourse, are used to model the dependence of production decisions on the uncertain parameters. When applied to an industrial-scale compressor train case study, the proposed model achieves significant cost savings compared with a model that does not consider integer recourse.

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Computers & Chemical Engineering

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀