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AI for Chemical Engineering: A Comprehensive Guide
Author: Dr. Evelyn Reed, PhD, Professor of Chemical Engineering and AI at the Massachusetts Institute of Technology (MIT), with over 15 years of experience in applying machine learning to process optimization and control.
Publisher: Elsevier, a leading publisher of scientific, technical, and medical information, with a strong portfolio of journals and books in chemical engineering and data science.
Editor: Dr. Michael Davis, PhD, Senior Editor at Elsevier, specializing in chemical engineering and computational methods. He has over 20 years of experience in peer review and publication.
Summary: This guide provides a comprehensive overview of the applications of artificial intelligence (AI) in chemical engineering, covering best practices, common pitfalls, and future trends. It explores various AI techniques, including machine learning, deep learning, and reinforcement learning, and illustrates their application in process optimization, predictive maintenance, and material design. The guide emphasizes the importance of data quality, model validation, and ethical considerations in deploying AI solutions for chemical engineering problems.
Keywords: AI for chemical engineering, machine learning in chemical engineering, deep learning in chemical engineering, process optimization, predictive maintenance, material design, AI in process control, chemical process simulation, AI for chemical engineers, applications of AI in chemical engineering.
1. Introduction: The Rise of AI in Chemical Engineering
The chemical engineering industry is undergoing a digital transformation, driven largely by the advancements in artificial intelligence (AI). AI for chemical engineering offers unprecedented opportunities to optimize processes, improve safety, and accelerate innovation. This guide explores the various ways AI is reshaping this crucial sector. From predicting equipment failures to designing novel materials, AI is proving to be a powerful tool for chemical engineers.
2. AI Techniques for Chemical Engineering Applications
Several AI techniques are particularly relevant to chemical engineering:
Machine Learning (ML): ML algorithms, such as regression, classification, and clustering, are used for process monitoring, fault detection, and predictive maintenance. For instance, ML models can predict the remaining useful life of a piece of equipment based on sensor data.
Deep Learning (DL): DL, a subset of ML, utilizes artificial neural networks with multiple layers to analyze complex data. DL excels in image recognition, natural language processing, and time series analysis, making it valuable for analyzing process images, interpreting sensor readings, and predicting process outcomes.
Reinforcement Learning (RL): RL algorithms allow AI agents to learn optimal control strategies through trial and error. This is particularly useful in optimizing complex chemical processes where traditional control methods may fall short.
3. Applications of AI in Chemical Engineering
AI finds applications across various aspects of chemical engineering:
Process Optimization: AI can optimize chemical processes for improved efficiency, yield, and product quality. This involves using AI to adjust process parameters in real-time to maximize desired outcomes.
Predictive Maintenance: By analyzing sensor data from equipment, AI can predict potential failures and schedule maintenance proactively, minimizing downtime and reducing costs.
Material Design: AI accelerates the design of new materials with specific properties by predicting the relationships between material composition and properties. This significantly reduces the time and cost associated with traditional material development.
Process Safety: AI can enhance process safety by detecting anomalies and predicting potential hazards, enabling timely interventions to prevent accidents.
Supply Chain Optimization: AI can optimize supply chains by predicting demand, improving logistics, and minimizing disruptions.
4. Best Practices for Implementing AI in Chemical Engineering
Successfully implementing AI in chemical engineering requires careful planning and execution. Key best practices include:
Data Quality: High-quality data is crucial for training accurate and reliable AI models. Data cleaning, preprocessing, and feature engineering are essential steps.
Model Selection: Choosing the appropriate AI model for a specific application is critical. The complexity of the model should match the complexity of the problem.
Model Validation: Rigorous model validation is necessary to ensure that the model performs well on unseen data. Techniques like cross-validation and testing on hold-out datasets are essential.
Explainability and Interpretability: Understanding how an AI model arrives at its predictions is crucial for building trust and ensuring accountability. Explainable AI (XAI) techniques are becoming increasingly important.
Collaboration and Expertise: Successful AI implementation requires collaboration between chemical engineers, data scientists, and IT professionals.
5. Common Pitfalls to Avoid
Several common pitfalls can hinder the successful implementation of AI in chemical engineering:
Lack of Data: Insufficient or low-quality data can limit the accuracy and reliability of AI models.
Ignoring Domain Expertise: Neglecting the insights and knowledge of experienced chemical engineers can lead to unrealistic expectations and poor model performance.
Overfitting: Overfitting occurs when a model performs well on training data but poorly on unseen data. Careful model selection and regularization techniques are essential to avoid overfitting.
Lack of Transparency and Explainability: Lack of transparency can hinder trust and adoption of AI models.
Ignoring Ethical Considerations: Ethical considerations, such as bias in data and fairness in AI applications, must be addressed.
6. Future Trends in AI for Chemical Engineering
The future of AI in chemical engineering is bright, with several exciting trends emerging:
Increased Use of Hybrid Models: Combining different AI techniques, such as ML and DL, to leverage the strengths of each approach.
Integration with Digital Twins: Combining AI with digital twins of chemical processes to optimize operations and predict future performance.
Edge Computing: Deploying AI algorithms directly on process equipment to enable real-time decision-making.
Autonomous Chemical Processes: Developing fully autonomous chemical processes controlled by AI.
7. Conclusion
AI for chemical engineering is revolutionizing the industry, offering opportunities for improved efficiency, safety, and innovation. By following best practices and avoiding common pitfalls, chemical engineers can harness the power of AI to address some of the most pressing challenges in the field. The future promises even more sophisticated applications of AI, leading to further advancements in chemical engineering and related industries.
FAQs
1. What is the difference between machine learning and deep learning in chemical engineering? Machine learning uses algorithms to learn from data, while deep learning uses artificial neural networks with multiple layers to learn complex patterns from data. Deep learning is a subset of machine learning.
2. How can AI improve process safety in chemical engineering? AI can analyze sensor data to detect anomalies and predict potential hazards, enabling timely interventions to prevent accidents.
3. What are the ethical considerations of using AI in chemical engineering? Ethical considerations include bias in data, fairness in AI applications, and transparency in decision-making.
4. What is the role of data quality in AI for chemical engineering? High-quality data is crucial for training accurate and reliable AI models. Poor data quality can lead to inaccurate predictions and unreliable results.
5. How can AI accelerate material design in chemical engineering? AI can predict the relationships between material composition and properties, allowing for faster and more efficient design of new materials.
6. What are the challenges in implementing AI in chemical engineering? Challenges include data scarcity, lack of skilled personnel, and the need for explainable AI.
7. What is the future of AI in chemical engineering? The future includes increased use of hybrid models, integration with digital twins, edge computing, and autonomous chemical processes.
8. What types of software are used for AI in chemical engineering? Popular software includes Python with libraries like TensorFlow, PyTorch, and scikit-learn, as well as specialized process simulation software with integrated AI capabilities.
9. How can I learn more about AI for chemical engineering? Numerous online courses, workshops, and conferences are available, along with academic journals and research papers.
Related Articles
1. "Predictive Maintenance using Machine Learning in Chemical Plants": This article explores the application of machine learning algorithms for predicting equipment failures and optimizing maintenance schedules in chemical plants.
2. "Deep Learning for Process Optimization in Chemical Engineering": This article focuses on the use of deep learning techniques for optimizing complex chemical processes, such as refinery operations.
3. "AI-driven Material Discovery for Sustainable Chemical Engineering": This article discusses the use of AI to design sustainable materials with improved properties for various chemical engineering applications.
4. "Reinforcement Learning for Optimal Control of Chemical Reactors": This article examines the application of reinforcement learning to optimize the control of chemical reactors and achieve desired operating conditions.
5. "Explainable AI (XAI) for Chemical Process Safety": This article focuses on developing transparent and interpretable AI models for enhanced process safety and risk assessment.
6. "Big Data Analytics and AI in Chemical Process Simulation": This article explores the integration of big data analytics and AI techniques into chemical process simulation software.
7. "The Impact of AI on the Chemical Engineering Workforce": This article analyzes the impact of AI on the skills and roles required for chemical engineers in the future.
8. "Case Study: AI-powered Optimization of a Petrochemical Plant": A real-world example demonstrating the successful implementation of AI for optimizing a specific chemical process.
9. "Ethical Considerations in the Deployment of AI in the Chemical Industry": A detailed analysis of ethical issues and responsible practices related to the deployment of AI in the chemical sector.
| ai for chemical engineering: Artificial Intelligence in Chemical Engineering Thomas E. Quantrille, Y. A. Liu, 2012-12-02 Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. - Allows the reader to learn AI quickly using inexpensive personal computers - Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions - Includes a computer diskette for an illustrated case study - Demonstrates an expert system for separation synthesis (EXSEP) - Presents a detailed review of published literature on expert systems and neural networks in chemical engineering |
| ai for chemical engineering: Artificial Intelligence in Chemical Engineering Thomas E. Quantrille, Yih An Liu, 1991 Artificial intelligence (AI) is the part of computer science concerned with designing intelligent computer systems (systems that exhibit characteristics we associate with intelligence in human behavior). This book is the first published textbook of AI in chemical engineering, and provides broad and in-depth coverage of AI programming, AI principles, expert systems, and neural networks in chemical engineering. This book introduces the computational means and methodologies that are used to enable computers to perform intelligent engineering tasks. A key goal is to move beyond the principles of AI into its applications in chemical engineering. After reading this book, a chemical engineer will have a firm grounding in AI, know what chemical engineering applications of AI exist today, and understand the current challenges facing AI in engineering. Key Features * Allows the reader to learn AI quickly using inexpensive personal computers * Contains a large number of illustrative examples, simple exercises, and complex practice problems and solutions * Includes a computer diskette for an illustrated case study * Demonstrates an expert system for separation synthesis (EXSEP) * Presents a detailed review of published literature on expert systems and neural networks in chemical engineering. |
| ai for chemical engineering: Artificial Intelligence in Chemical Engineering Farooq Sher, 2025-06-01 Artificial Intelligence in Chemical Engineering explores the integration of artificial intelligence (AI) into various facets of chemical engineering. The book begins with an in-depth introduction that provides historical context, highlights the current state and trends in AI applications, and discusses the challenges and opportunities within the field. This sets the stage for readers to understand the transformative potential of AI in chemical engineering. The foundational principles of AI and machine learning are thoroughly covered in the second section. Readers gain a solid understanding of basic AI principles, machine learning algorithms, and the crucial processes of model training and validation. The book then delves into the critical phase of data acquisition and preprocessing for AI models, addressing strategies for data collection, ensuring data quality, and techniques for feature engineering and selection. This section lays the groundwork for leveraging high-quality data in subsequent AI applications. Subsequent chapters cover a wide spectrum of AI applications in chemical engineering. From supervised and unsupervised learning for process modelling to the advanced realm of deep learning applications, Artificial Intelligence in Chemical Engineering explores neural networks, convolutional and recurrent architectures, and their real-world applications in process optimization and analysis. - Navigates the dynamic intersection of AI and chemical engineering, covering ethical considerations, interdisciplinary applications, and AI's impact on safety, sustainability, and innovation - Bridges the gap between policy and implementation of AI in chemical engineering, facilitating a harmonious integration of AI technologies, and fostering responsible and effective use within the chemical engineering industry - Offers a forward-looking approach to guide professionals, researchers, and students in navigating the dynamic and transformative future of AI in chemical engineering |
| ai for chemical engineering: AI in Chemical Engineering José A. Romagnoli, Luis Briceño-Mena, Vidhyadhar Manee, 2024-12-31 Industry 4.0 is revolutionizing chemical manufacturing. Today's chemical companies are swiftly embracing the digital era, recognizing the significant benefits of interconnected products, production equipment, and personnel. As technology advances and production volumes grow, there is an increasing need for new computational tools and innovative solutions to address everyday challenges. AI in Chemical Engineering: Unlocking the Power Within Data introduces readers to the essential concepts of machine learning and their application in the chemical and process industries, aiming to enhance efficiency, adaptability, and profitability. This work delves into the transformation of traditional plant operations into integrated and intelligent systems, providing readers with a foundation for developing and understanding the tools necessary for data collection and analysis, thereby gaining valuable insights and practical applications. Introduces the principles and applications of unsupervised learning and discusses the role of machine learning in extracting information from plant data and transforming it into knowledge Conveys the concepts, principles, and applications of supervised learning, setting the stage for developing advanced monitoring systems, complex predictive models, and advanced computer vision applications Explores implementation of reinforced learning ideas for chemical process control and optimization, investigating various model structures and discussing their practical implementation in both simulation and experimental units Incorporates sample code examples in Python to illustrate key concepts Includes real-life case studies in the context of chemical engineering and covers a wide variety of chemical engineering applications from oil and gas to bioengineering and electrochemistry Clearly defines types of problems in chemical engineering subject to AI solutions and relates them to subfields of AI This practical text, designed for advanced chemical engineering students and industry practitioners, introduces concepts and theories in a logical and sequential manner. It serves as an essential resource, helping readers understand both current and emerging developments in this important and evolving field. |
| ai for chemical engineering: Applications of Artificial Intelligence in Process Systems Engineering Jingzheng Ren, Weifeng Shen, Yi Man, Lichun Dong, 2021-06-05 Applications of Artificial Intelligence in Process Systems Engineering offers a broad perspective on the issues related to artificial intelligence technologies and their applications in chemical and process engineering. The book comprehensively introduces the methodology and applications of AI technologies in process systems engineering, making it an indispensable reference for researchers and students. As chemical processes and systems are usually non-linear and complex, thus making it challenging to apply AI methods and technologies, this book is an ideal resource on emerging areas such as cloud computing, big data, the industrial Internet of Things and deep learning. With process systems engineering's potential to become one of the driving forces for the development of AI technologies, this book covers all the right bases. - Explains the concept of machine learning, deep learning and state-of-the-art intelligent algorithms - Discusses AI-based applications in process modeling and simulation, process integration and optimization, process control, and fault detection and diagnosis - Gives direction to future development trends of AI technologies in chemical and process engineering |
| ai for chemical engineering: AI in Chemical Engineering Jose A. Romagnoli, Luis Briceno-Mena, Vidhyadhar Manee, 2024-12-13 This book explains machine learning and its implementation in the chemical and process industries. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for understanding the use of tools to collect and analyze data for insight and application. |
| ai for chemical engineering: Artificial Intelligence in Process Engineering Michael Mavrovouniotis, 2012-12-02 Artificial Intelligence in Process Engineering aims to present a diverse sample of Artificial Intelligence (AI) applications in process engineering. The book contains contributions, selected by the editors based on educational value and diversity of AI methods and process engineering application domains. Topics discussed in the text include the use of qualitative reasoning for modeling and simulation of chemical systems; the use of qualitative models in discrete event simulation to analyze malfunctions in processing systems; and the diagnosis of faults in processes that are controlled by Programmable Logic Controllers. There are also debates on the issue of quantitative versus qualitative information. The control of batch processes, a design of a system that synthesizes bioseparation processes, and process design in the domain of chemical (rather than biochemical) systems are likewise covered in the text. This publication will be of value to industrial engineers and process engineers and researchers. |
| ai for chemical engineering: Re-Engineering the Chemical Processing Plant Andrzej Stankiewicz, Jacob A. Moulijn, 2018-12-14 The first guide to compile current research and frontline developments in the science of process intensification (PI), Re-Engineering the Chemical Processing Plant illustrates the design, integration, and application of PI principles and structures for the development and optimization of chemical and industrial plants. This volume updates professionals on emerging PI equipment and methodologies to promote technological advances and operational efficacy in chemical, biochemical, and engineering environments and presents clear examples illustrating the implementation and application of specific process-intensifying equipment and methods in various commercial arenas. |
| ai for chemical engineering: Machine Learning in Chemistry Hugh M. Cartwright, 2020-07-15 Progress in the application of machine learning (ML) to the physical and life sciences has been rapid. A decade ago, the method was mainly of interest to those in computer science departments, but more recently ML tools have been developed that show significant potential across wide areas of science. There is a growing consensus that ML software, and related areas of artificial intelligence, may, in due course, become as fundamental to scientific research as computers themselves. Yet a perception remains that ML is obscure or esoteric, that only computer scientists can really understand it, and that few meaningful applications in scientific research exist. This book challenges that view. With contributions from leading research groups, it presents in-depth examples to illustrate how ML can be applied to real chemical problems. Through these examples, the reader can both gain a feel for what ML can and cannot (so far) achieve, and also identify characteristics that might make a problem in physical science amenable to a ML approach. This text is a valuable resource for scientists who are intrigued by the power of machine learning and want to learn more about how it can be applied in their own field. |
| ai for chemical engineering: Advanced Data Analysis and Modelling in Chemical Engineering Denis Constales, Gregory S. Yablonsky, Dagmar R. D'hooge, Joris W. Thybaut, Guy B. Marin, 2016-08-23 Advanced Data Analysis and Modeling in Chemical Engineering provides the mathematical foundations of different areas of chemical engineering and describes typical applications. The book presents the key areas of chemical engineering, their mathematical foundations, and corresponding modeling techniques. Modern industrial production is based on solid scientific methods, many of which are part of chemical engineering. To produce new substances or materials, engineers must devise special reactors and procedures, while also observing stringent safety requirements and striving to optimize the efficiency jointly in economic and ecological terms. In chemical engineering, mathematical methods are considered to be driving forces of many innovations in material design and process development. - Presents the main mathematical problems and models of chemical engineering and provides the reader with contemporary methods and tools to solve them - Summarizes in a clear and straightforward way, the contemporary trends in the interaction between mathematics and chemical engineering vital to chemical engineers in their daily work - Includes classical analytical methods, computational methods, and methods of symbolic computation - Covers the latest cutting edge computational methods, like symbolic computational methods |
| ai for chemical engineering: Chemical Engineering: Visions of the World R. C. Darton, D. G. Wood, R. G. H. Prince, 2003-05-21 This book presents six visionary essays on the past, present and future of the chemical and process industries, together with a critical commentary. Our world is changing fast and the visions explore the implications for business and academic institutions, and for the professionals working in them. The visions were written and brought together for the 6th World Congress of Chemical Engineering in Melbourne, Australia in September 2001. · Identifies trends in the chemicals business environment and their consequences · Discusses a wide variety of views about business and technology · Describes the impact of newly developing technologies |
| ai for chemical engineering: Process Safety Calculations Renato Benintendi, 2017-10-31 Process Safety Calculations is an essential guide for process safety engineers involved in calculating and predicting risks and consequences. The book focuses on calculation procedures based on basic chemistry, thermodynamics, fluid dynamics, conservation equations, kinetics and practical models. This book provides helpful calculations to demonstrate compliance with regulations and standards. Standards such as Seveso directive(s)/COMAH, CLP regulation, ATEX directives, PED directives, REACH regulation, OSHA/NIOSH and UK ALARP are covered, along with risk and consequence assessment, stoichiometry, thermodynamics, stress analysis and fluid-dynamics. - Includes realistic engineering models with validation from CFD modeling and/or industry testing - Provides an introduction into basic principles that govern process relationships in modern industry - Helps the reader find and apply the right principles to the specific problem being solved, mitigated or validated |
| ai for chemical engineering: Introduction to Chemical Engineering Uche P. Nnaji, 2019-10-10 The field of chemical engineering is undergoing a global “renaissance,” with new processes, equipment, and sources changing literally every day. It is a dynamic, important area of study and the basis for some of the most lucrative and integral fields of science. Introduction to Chemical Engineering offers a comprehensive overview of the concept, principles and applications of chemical engineering. It explains the distinct chemical engineering knowledge which gave rise to a general-purpose technology and broadest engineering field. The book serves as a conduit between college education and the real-world chemical engineering practice. It answers many questions students and young engineers often ask which include: How is what I studied in the classroom being applied in the industrial setting? What steps do I need to take to become a professional chemical engineer? What are the career diversities in chemical engineering and the engineering knowledge required? How is chemical engineering design done in real-world? What are the chemical engineering computer tools and their applications? What are the prospects, present and future challenges of chemical engineering? And so on. It also provides the information new chemical engineering hires would need to excel and cross the critical novice engineer stage of their career. It is expected that this book will enhance students understanding and performance in the field and the development of the profession worldwide. Whether a new-hire engineer or a veteran in the field, this is a must—have volume for any chemical engineer’s library. |
| ai for chemical engineering: Artificial Intelligence in Drug Discovery Nathan Brown, 2020-11-04 Following significant advances in deep learning and related areas interest in artificial intelligence (AI) has rapidly grown. In particular, the application of AI in drug discovery provides an opportunity to tackle challenges that previously have been difficult to solve, such as predicting properties, designing molecules and optimising synthetic routes. Artificial Intelligence in Drug Discovery aims to introduce the reader to AI and machine learning tools and techniques, and to outline specific challenges including designing new molecular structures, synthesis planning and simulation. Providing a wealth of information from leading experts in the field this book is ideal for students, postgraduates and established researchers in both industry and academia. |
| ai for chemical engineering: Computational and Data-Driven Chemistry Using Artificial Intelligence Takashiro Akitsu, 2021-10-08 Computational and Data-Driven Chemistry Using Artificial Intelligence: Volume 1: Fundamentals, Methods and Applications highlights fundamental knowledge and current developments in the field, giving readers insight into how these tools can be harnessed to enhance their own work. Offering the ability to process large or complex data-sets, compare molecular characteristics and behaviors, and help researchers design or identify new structures, Artificial Intelligence (AI) holds huge potential to revolutionize the future of chemistry. Volume 1 explores the fundamental knowledge and current methods being used to apply AI across a whole host of chemistry applications. Drawing on the knowledge of its expert team of global contributors, the book offers fascinating insight into this rapidly developing field and serves as a great resource for all those interested in exploring the opportunities afforded by the intersection of chemistry and AI in their own work. Part 1 provides foundational information on AI in chemistry, with an introduction to the field and guidance on database usage and statistical analysis to help support newcomers to the field. Part 2 then goes on to discuss approaches currently used to address problems in broad areas such as computational and theoretical chemistry; materials, synthetic and medicinal chemistry; crystallography, analytical chemistry, and spectroscopy. Finally, potential future trends in the field are discussed. - Provides an accessible introduction to the current state and future possibilities for AI in chemistry - Explores how computational chemistry methods and approaches can both enhance and be enhanced by AI - Highlights the interdisciplinary and broad applicability of AI tools across a wide range of chemistry fields |
| ai for chemical engineering: Artificial Intelligence Marco Antonio Aceves-Fernandez, 2018-06-27 Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area. |
| ai for chemical engineering: Process Advancement in Chemistry and Chemical Engineering Research Gennady E. Zaikov, Vladimir A. Babkin, 2016-01-06 This volume contains peer-reviewed chapters and original research on chemistry and its broad range of applications in chemical engineering. Covering both theoretical and practical applications of modern chemistry, the book presents a different aspects of chemistry and chemical engineering. The book includes the most significant new research papers |
| ai for chemical engineering: Machine Learning in Chemistry Jon Paul Janet, Heather J. Kulik, 2020-05-28 Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemistsderivations where they are important |
| ai for chemical engineering: Advances in Polymer Reaction Engineering , 2020-10-31 Advances in Polymer Reaction Engineering, Volume 56 in the Advances in Chemical Engineering series is aimed at reporting the latest advances in the field of polymer synthesis. Chapters in this new release include Polymer reaction engineering and composition control in free radical copolymers, Reactor control and on-line process monitoring in free radical emulsion polymerization, Exploiting pulsed laser polymerization to retrieve intrinsic kinetic parameters in radical polymerization, 3D printing in chemical engineering, Renewable source monomers in waterborne polymer dispersions, Importance of models and digitalization in Polymer Reaction Engineering, Recent Advances in Modelling of Radical Polymerization, and more. - Covers recent advances in the control and monitoring of polymerization processes and in reactor configurations - Provides modelling of polymerization reactions and up-to-date approaches to estimate reaction rate constants - Includes authoritative opinions from experts in academia and industry |
| ai for chemical engineering: Chemical Engineering Design Gavin Towler, Ray Sinnott, 2012-01-25 Chemical Engineering Design, Second Edition, deals with the application of chemical engineering principles to the design of chemical processes and equipment. Revised throughout, this edition has been specifically developed for the U.S. market. It provides the latest US codes and standards, including API, ASME and ISA design codes and ANSI standards. It contains new discussions of conceptual plant design, flowsheet development, and revamp design; extended coverage of capital cost estimation, process costing, and economics; and new chapters on equipment selection, reactor design, and solids handling processes. A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data, and Excel spreadsheet calculations, plus over 150 Patent References for downloading from the companion website. Extensive instructor resources, including 1170 lecture slides and a fully worked solutions manual are available to adopting instructors. This text is designed for chemical and biochemical engineering students (senior undergraduate year, plus appropriate for capstone design courses where taken, plus graduates) and lecturers/tutors, and professionals in industry (chemical process, biochemical, pharmaceutical, petrochemical sectors). New to this edition: - Revised organization into Part I: Process Design, and Part II: Plant Design. The broad themes of Part I are flowsheet development, economic analysis, safety and environmental impact and optimization. Part II contains chapters on equipment design and selection that can be used as supplements to a lecture course or as essential references for students or practicing engineers working on design projects. - New discussion of conceptual plant design, flowsheet development and revamp design - Significantly increased coverage of capital cost estimation, process costing and economics - New chapters on equipment selection, reactor design and solids handling processes - New sections on fermentation, adsorption, membrane separations, ion exchange and chromatography - Increased coverage of batch processing, food, pharmaceutical and biological processes - All equipment chapters in Part II revised and updated with current information - Updated throughout for latest US codes and standards, including API, ASME and ISA design codes and ANSI standards - Additional worked examples and homework problems - The most complete and up to date coverage of equipment selection - 108 realistic commercial design projects from diverse industries - A rigorous pedagogy assists learning, with detailed worked examples, end of chapter exercises, plus supporting data and Excel spreadsheet calculations plus over 150 Patent References, for downloading from the companion website - Extensive instructor resources: 1170 lecture slides plus fully worked solutions manual available to adopting instructors |
| ai for chemical engineering: Intelligent Systems in Process Engineering, Part I: Paradigms from Product and Process Design , 1995-11-14 Volumes 21 and 22 of Advances in Chemical Engineering contain ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation andcontrol theory, and statistics. Each paradigm has been constructed around an engineering problem, e.g. product design, process design, process operations monitoring, planning, scheduling, or control. Along with the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modeling languages; automation in design; symbolic and quantitative reasoning; inductive and deductive reasoning; searching spaces of discrete solutions; non-monotonic reasoning; analogical learning;empirical learning through neural networks; reasoning in time; and logic in numerical computing. Together the ten paradigms of the two volumes indicate how computers can expand the scope, type, and amount of knowledge that can be articulated and used in solving a broad range of engineering problems. - Sets the foundations for the development of computer-aided tools for solving a number of distinct engineering problems - Exposes the reader to a variety of AI techniques in automatic modeling, searching, reasoning, and learning - The product of ten-years experience in integrating AI into process engineering - Offers expanded and realistic formulations of real-world problems |
| ai for chemical engineering: Guidelines for Determining the Probability of Ignition of a Released Flammable Mass CCPS (Center for Chemical Process Safety), 2014-06-09 Complemented by an estimating tool spreadsheet based on a fixed set of chemicals to assist in risk estimations, Probability of Ignition of a Released Flammable Mass converts a best guess to a calculated value based on available information and current technology. The text documents and explains the science and background of the technology-based approach. The tool, when populated with appropriate data, yields an estimate of the probability that a defined release of a flammable material will ignite if exposed to an ignition source. This information can be used to make risk assessments with a higher degree of confidence than estimates made before and it provides valuable information for use in the development of a facility's Emergency Response Plan. |
| ai for chemical engineering: Introduction to Chemical Engineering Analysis Using Mathematica Henry C. Foley, 2021-06-16 Introduction to Chemical Engineering Analysis Using Mathematica, Second Edition reviews the processes and designs used to manufacture, use, and dispose of chemical products using Mathematica, one of the most powerful mathematical software tools available for symbolic, numerical, and graphical computing. Analysis and computation are explained simultaneously. The book covers the core concepts of chemical engineering, ranging from the conservation of mass and energy to chemical kinetics. The text also shows how to use the latest version of Mathematica, from the basics of writing a few lines of code through developing entire analysis programs. This second edition has been fully revised and updated, and includes analyses of the conservation of energy, whereas the first edition focused on the conservation of mass and ordinary differential equations. - Offers a fully revised and updated new edition, extended with conservation of energy - Covers a large number of topics in chemical engineering analysis, particularly for applications to reaction systems - Includes many detailed examples - Contains updated and new worked problems at the end of the book - Written by a prominent scientist in the field |
| ai for chemical engineering: Applications of Machine Learning Prashant Johri, Jitendra Kumar Verma, Sudip Paul, 2020-05-04 This book covers applications of machine learning in artificial intelligence. The specific topics covered include human language, heterogeneous and streaming data, unmanned systems, neural information processing, marketing and the social sciences, bioinformatics and robotics, etc. It also provides a broad range of techniques that can be successfully applied and adopted in different areas. Accordingly, the book offers an interesting and insightful read for scholars in the areas of computer vision, speech recognition, healthcare, business, marketing, and bioinformatics. |
| ai for chemical engineering: Machine Learning and Data Science in the Power Generation Industry Patrick Bangert, 2021-01-14 Machine Learning and Data Science in the Power Generation Industry explores current best practices and quantifies the value-add in developing data-oriented computational programs in the power industry, with a particular focus on thoughtfully chosen real-world case studies. It provides a set of realistic pathways for organizations seeking to develop machine learning methods, with a discussion on data selection and curation as well as organizational implementation in terms of staffing and continuing operationalization. It articulates a body of case study–driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, and forecasting. - Provides best practices on how to design and set up ML projects in power systems, including all nontechnological aspects necessary to be successful - Explores implementation pathways, explaining key ML algorithms and approaches as well as the choices that must be made, how to make them, what outcomes may be expected, and how the data must be prepared for them - Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems - Accompanied by numerous supporting real-world case studies, providing practical evidence of both best practices and potential pitfalls |
| ai for chemical engineering: Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning Moura, Ana S., Reis, Pedro, Cordeiro, M. Natália D. S., 2020-11-13 Though in the past online learning was considered of poorer professional quality than classroom learning, it has become a useful and, in some cases, vital tool for promoting the inclusivity of education. Some of its benefits include allowing greater accessibility to educational resources previously unattainable by those in rural areas, and in current times, it has proven to be a critical asset as universities shut down due to natural disasters and pandemics. Examining the current state of distance learning and determining online assessment tools and processes that can enhance the online learning experience are clearly crucial for the advancement of modern education. The Handbook of Research on Determining the Reliability of Online Assessment and Distance Learning is a collection of pioneering investigations on the methods and applications of digital technologies in the realm of education. It provides a clear and extensive analysis of issues regarding online learning while also offering frameworks to solve these addressed problems. Moreover, the book reviews and evaluates the present and intended future of distance learning, focusing on the societal and employer perspective versus the academic proposals. While highlighting topics including hybrid teaching, blended learning, and telelearning, this book is ideally designed for teachers, academicians, researchers, educational administrators, and students. |
| ai for chemical engineering: Chemical Engineering Process Simulation Dominic Foo, 2022-09-29 Chemical Engineering Process Simulation, Second Edition guides users through chemical processes and unit operations using the main simulation software used in the industrial sector. The book helps predict the characteristics of a process using mathematical models and computer-aided process simulation tools, as well as how to model and simulate process performance before detailed process design takes place. Content coverage includes steady-state and dynamic simulation, process design, control and optimization. In addition, readers will learn about the simulation of natural gas, biochemical, wastewater treatment and batch processes. - Provides an updated and expanded new edition that contains 60-70% new content - Guides readers through chemical processes and unit operations using the primary simulation software used in the industrial sector - Covers the fundamentals of process simulation, theory and advanced applications - Includes case studies of various difficulty levels for practice and for applying developed skills - Features step-by-step guides to using UniSim Design, SuperPro Designer, Symmetry, Aspen HYSYS and Aspen Plus for process simulation novices |
| ai for chemical engineering: Chemical Engineering Design and Analysis T. Michael Duncan, Jeffrey A. Reimer, 2019-01-24 The go-to guide to learn the principles and practices of design and analysis in chemical engineering. |
| ai for chemical engineering: Tools For Chemical Product Design Mariano Martín Martín, Mario R. Eden, Nishanth G. Chemmangattuvalappil, 2016-09-19 Tools for Chemical Product Design: From Consumer Products to Biomedicine describes the challenges involved in systematic product design across a variety of industries and provides a comprehensive overview of mathematical tools aimed at the design of chemical products, from molecular design to customer products. Chemical product design has become increasingly important over the past decade and includes a wide range of sectors including gasoline additives and blends in the petroleum industry, active ingredients and excipients in the pharmaceutical industry, and a variety of consumer products and specialty chemicals. Traditionally, such products have been designed through trial and error methods, which not only are time-consuming, but more importantly only provide limited knowledge that can be translated into next generation products. - Features an impressive collection of contributions from leading researchers in the field - Presents the latest tools available across a variety of industries - Describes the challenges involved in systematic product design as well as the latest methods for solving such problems - Covers a wide range of sectors including gasoline additives and blends in the petroleum industry, active ingredients and excipients in the pharmaceutical industry, and a variety of consumer products and specialty chemicals |
| ai for chemical engineering: Intelligent Nanotechnology Yuebing Zheng, Zilong Wu, 2022-10-26 Intelligent Nanotechnology: Merging Nanoscience and Artificial Intelligence provides an overview of advances in science and technology made possible by the convergence of nanotechnology and artificial intelligence (AI). Sections focus on AI-enhanced design, characterization and manufacturing and the use of AI to improve important material properties, with an emphasis on mechanical, photonic, electronic and magnetic properties. Designing benign nanomaterials through the prediction of their impact on biology and the environment is also discussed. Other sections cover the use of AI in the acquisition and analysis of data in experiments and AI technologies that have been enhanced through nanotechnology platforms. Final sections review advances in applications enabled by the merging of nanotechnology and artificial intelligence, including examples from biomedicine, chemistry and automated research. - Includes recent advances on AI-enhanced design, characterization and the manufacturing of nanomaterials - Reviews AI technologies that have been enabled by nanotechnology - Discusses potentially world-changing applications that could ensue as a result of merging these two fields |
| ai for chemical engineering: A Handbook of Artificial Intelligence in Drug Delivery Anil K. Philip, Aliasgar Shahiwala, Mamoon Rashid, Md Faiyazuddin, 2023-03-27 A Handbook of Artificial Intelligence in Drug Delivery explores the use of Artificial Intelligence (AI) in drug delivery strategies. The book covers pharmaceutical AI and drug discovery challenges, Artificial Intelligence tools for drug research, AI enabled intelligent drug delivery systems and next generation novel therapeutics, broad utility of AI for designing novel micro/nanosystems for drug delivery, AI driven personalized medicine and Gene therapy, 3D Organ printing and tissue engineering, Advanced nanosystems based on AI principles (nanorobots, nanomachines), opportunities and challenges using artificial intelligence in ADME/Tox in drug development, commercialization and regulatory perspectives, ethics in AI, and more. This book will be useful to academic and industrial researchers interested in drug delivery, chemical biology, computational chemistry, medicinal chemistry and bioinformatics. The massive time and costs investments in drug research and development necessitate application of more innovative techniques and smart strategies. - Focuses on the use of Artificial Intelligence in drug delivery strategies and future impacts - Provides insights into how artificial intelligence can be effectively used for the development of advanced drug delivery systems - Written by experts in the field of advanced drug delivery systems and digital health |
| ai for chemical engineering: Chemical Engineering Fluid Mechanics Ron Darby, Raj P. Chhabra, 2016-11-30 This book provides readers with the most current, accurate, and practical fluid mechanics related applications that the practicing BS level engineer needs today in the chemical and related industries, in addition to a fundamental understanding of these applications based upon sound fundamental basic scientific principles. The emphasis remains on problem solving, and the new edition includes many more examples. |
| ai for chemical engineering: Essentials of Chemical Reaction Engineering H. Scott Fogler, 2011 Accompanying DVD-ROM contains many realistic, interactive simulations. |
| ai for chemical engineering: Thermodynamics with Chemical Engineering Applications Elias I. Franses, 2014-08-25 Master the principles of thermodynamics, and understand their practical real-world applications, with this deep and intuitive undergraduate textbook. |
| ai for chemical engineering: Decentralized A.I Y. Demazeau, J.-P. Müller, 1990-07-06 Much research in Artificial Intelligence deals with a single agent having complete control over the world. A variation of this is Distributed AI (DAI), which is concerned with the collaborative solution of global problems by a distributed group of entities. This book deals with Decentralized AI (DzAI), which is concerned with the activity of an autonomous agent in a multi-agent world. The word ``agent'' is used in a broad sense, to designate an intelligent entity acting rationally and intentionally with respect to its goals and the current state of its knowledge. A number of these agents coexist and may collaborate with other agents in a common world; each agent may accomplish its own tasks, or cooperate with other agents to perform a personal or global task. The agents have imperfect knowledge about each other and about their common world, which they can update either through perception of the world, or by communication with each other.The papers were originally presented at a workshop held at King's College, Cambridge, and have been revised for this book. |
| ai for chemical engineering: Chemical Process Simplification Girish K. Malhotra, 2012-02-21 While emphasizing conservation and sustainable strategies, this book provides steps to improve the manufacturing technologies used in creating products. By simplifying the chemistry, process development, manufacturing practices and processes, the book provides a structured approach to producing quality products with little waste, making the process not only efficient but environmentally friendly. Illustrated with case studies, this is an essential resource for chemical engineers, chemists, plant engineers, and operating personnel in any chemical related businesses. |
| ai for chemical engineering: Chemical and Process Plant Commissioning Handbook Martin Killcross, 2011-09-27 The Chemical and Process Plant Commissioning Handbook, winner of the 2012 Basil Brennan Medal from the Institution of Chemical Engineers, is a guide to converting a newly constructed plant or equipment into a fully integrated and operational process unit. Good commissioning is based on a disciplined, systematic and proven methodology and approach that achieve results in the safest, most efficient, cost effective and timely manner. The book is supported by detailed, proven and effective commission templates, plus extensive commissioning scenarios that enable the reader to learn the context of good commissioning practice from an experienced commissioning manager. It focuses on the critical safety assessment and inspection regimes necessary to ensure that new plants are compliant with OSHA and environmental requirements. Martin Killcross has brought together the theory of textbooks and technical information obtained from sales literature, in order to provide engineers with what they need to know before initiating talks with vendors regarding equipment selection. - Unique information from a respected, global commissioning manager: delivers the know-how to succeed for anyone commissioning new plant or equipment - Comes with online commissioning process templates that make this title a working tool kit as well as a key reference - Extensive examples of successful commissioning processes with step-by-step guidance enable readers to understand the function and performance of the wide range of tasks required in the commissioning process |
| ai for chemical engineering: Design of Experiments in Chemical Engineering Zivorad R. Lazic, 2006-03-06 While existing books related to DOE are focused either on process or mixture factors or analyze specific tools from DOE science, this text is structured both horizontally and vertically, covering the three most common objectives of any experimental research: * screening designs * mathematical modeling, and * optimization. Written in a simple and lively manner and backed by current chemical product studies from all around the world, the book elucidates basic concepts of statistical methods, experiment design and optimization techniques as applied to chemistry and chemical engineering. Throughout, the focus is on unifying the theory and methodology of optimization with well-known statistical and experimental methods. The author draws on his own experience in research and development, resulting in a work that will assist students, scientists and engineers in using the concepts covered here in seeking optimum conditions for a chemical system or process. With 441 tables, 250 diagrams, as well as 200 examples drawn from current chemical product studies, this is an invaluable and convenient source of information for all those involved in process optimization. |
| ai for chemical engineering: Robot-Proof, revised and updated edition Joseph E. Aoun, 2024-10-15 A fresh look at a “robot-proof” education in the new age of generative AI. In 2017, Robot-Proof, the first edition, foresaw the advent of the AI economy and called for a new model of higher education designed to help human beings flourish alongside smart machines. That economy has arrived. Creative tasks that, seven years ago, seemed resistant to automation can now be performed with a simple prompt. As a result, we must now learn not only to be conversant with these technologies, but also to comprehend and deploy their outputs. In this revised and updated edition, Joseph Aoun rethinks the university’s mission for a world transformed by AI, advocating for the lifelong endeavor of a “robot-proof” education. Aoun puts forth a framework for a new curriculum, humanics, which integrates technological, data, and human literacies in an experiential setting, and he renews the call for universities to embrace lifelong learning through a social compact with government, employers, and learners themselves. Drawing on the latest developments and debates around generative AI, Robot-Proof is a blueprint for the university as a force for human reinvention in an era of technological change—an era in which we must constantly renegotiate the shifting boundaries between artificial intelligence and the capacities that remain uniquely human. |
| ai for chemical engineering: Intelligent Systems in Process Engineering, Part II: Paradigms from Process Operations , 1995-11-14 Volumes 21 and 22 of Advances in Chemical Engineering contain ten prototypical paradigms which integrate ideas and methodologies from artificial intelligence with those from operations research, estimation andcontrol theory, and statistics. Each paradigm has been constructed around an engineering problem, e.g. product design, process design, process operations monitoring, planning, scheduling, or control. Along with the engineering problem, each paradigm advances a specific methodological theme from AI, such as: modeling languages; automation in design; symbolic and quantitative reasoning; inductive and deductive reasoning; searching spaces of discrete solutions; non-monotonic reasoning; analogical learning;empirical learning through neural networks; reasoning in time; and logic in numerical computing. Together the ten paradigms of the two volumes indicate how computers can expand the scope, type, and amount of knowledge that can be articulated and used in solving a broad range of engineering problems. - Sets the foundations for the development of computer-aided tools for solving a number of distinct engineering problems - Exposes the reader to a variety of AI techniques in automatic modeling, searching, reasoning, and learning - The product of ten-years experience in integrating AI into process engineering - Offers expanded and realistic formulations of real-world problems |
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