AI in Chemical EngineeringJosé Alberto Romagnoli, Luis Briceno-Mena 0 / 4.0
"Chemical manufacturing is being transformed by Industry 4.0. Today's chemical companies are quickly adapting to the digital world, recognizing the power of connection among products, production equipment, and personnel. As technology evolves and manufactured volumes increase, new computational tools and innovative solutions for daily problems are required. AI in Chemical Engineering: Unlocking the Power Within Data familiarizes readers with the key concepts of machine learning and their implementation in the chemical and process industries for increased efficiency, adaptability, and profitability. It explores the evolution of traditional plant operation into an integrated and smart operational environment and provides readers with the basis for developing and understanding the use of tools to collect and analyze data for insight and application. 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. With concepts and theory introduced in a logical and sequential manner, this practical text is aimed at advanced students of chemical…
工业4.0正在推动化学制造业的转型。当今的化工企业正迅速适应数字化世界，认识到产品、生产设备和人员之间连接的力量。随着技术的进步和制造规模的扩大，新的计算工具和创新解决方案对于解决日常问题变得至关重要。《化学工程中的人工智能：释放数据内在潜力》帮助读者熟悉机器学习的核心概念及其在化工与过程工业中的实际应用，以提升效率、适应性和盈利能力。本书探讨了传统工厂运营向集成化、智能化运营环境的演变，并为读者提供了开发和使用工具来收集和分析数据、从中获取洞见并加以应用的基础。 书中介绍了无监督学习的原理与应用，并讨论了机器学习在从工厂数据中提取信息并将其转化为知识中的作用。阐述了监督学习的概念、原理与应用，为开发高级监控系统、复杂预测模型及先进计算机视觉应用奠定基础。探讨了强化学习思想在化工过程控制与优化中的实现，研究了多种模型结构，并讨论了它们在仿真与实验装置中的实际应用。书中包含 Python 示例代码，用以说明关键概念。结合化学工程领域的实际案例研究，涵盖从石油天然气到生物工程、电化学等多种应用场景。清晰界定了化学工程中适用于人工智能解决方案的问题类型，并将其与人工智能的子领域相联系。本书以逻辑性和顺序性的方式介绍概念与理论，是一本面向化学工程专业高年级学生的实用教材。
AI in Chemical Engineering Jose Alberto Romagnoli Luis Briceno-Mena Z-Library.pdf
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发表于 2025-9-18 22:54:39
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