Stochastic Modelling in Process Technology

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出版者:Elsevier Science Ltd
作者:Dehling, Herold G.
出品人:
页数:290
译者:
出版时间:2007-8
价格:$ 161.59
装帧:HRD
isbn号码:9780444520265
丛书系列:
图书标签:
  • 随机建模
  • 过程技术
  • 随机过程
  • 马尔可夫链
  • 排队论
  • 性能评估
  • 仿真
  • 可靠性
  • 概率模型
  • 工业工程
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具体描述

There is an ever increasing need for modelling complex processes reliably. Computational modelling techniques, such as CFD and MD may be used as tools to study specific systems, but their emergence has not decreased the need for generic, analytical process models. Multiphase and multicomponent systems, and high-intensity processes displaying a highly complex behaviour are becoming omnipresent in the processing industry. This book discusses an elegant, but little-known technique for formulating process models in process technology: stochastic process modelling. The technique is based on computing the probability distribution for a single particle's position in the process vessel, and/or the particle's properties, as a function of time, rather than - as is traditionally done - basing the model on the formulation and solution of differential conservation equations. Using this technique can greatly simplify the formulation of a model, and even make modelling possible for processes so complex that the traditional method is impracticable. Stochastic modelling has sporadically been used in various branches of process technology under various names and guises. This book gives, as the first, an overview of this work, and shows how these techniques are similar in nature, and make use of the same basic mathematical tools and techniques. The book also demonstrates how stochastic modelling may be implemented by describing example cases, and shows how a stochastic model may be formulated for a case, which cannot be described by formulating and solving differential balance equations. It features an introduction to stochastic process modelling as an alternative modelling technique. It shows how stochastic modelling may be succesful where the traditional technique fails. It offers an overview of stochastic modelling in process technology in the research literature. There is illustration of the principle by a wide range of practical examples. It includes in-depth and self-contained discussions. It points the way to both mathematical and technological research in a new, rewarding field.

这本书的核心主题围绕通过科学方法和模型分析过程技术中的不确定性进行研究,其深刻的理论基础旨在帮助读者深入理解复杂系统中变化与规律的内在联系。这一书不仅提供了详尽的历史背景,还系统阐述了各领域应用场景的具体需求,从实验设计到数据处理,再到模型验证,都体现出严谨且全面的思维方式。内容丰富,内容层次分明,适合希望深入探索过程技术研究方法的人士。 书中首先对传统分析工具进行了详细梳理和评估,强调在面对动态变化环境时,建立稳健模型的重要性。这部分章节不仅介绍了经典统计方法,还引入了现代数据分析的最新进展,如机器学习算法与大数据处理技术,为读者提供了多种工具选择的视野。书中的内容深入探讨了不同场景下模型选择的原则,帮助读者根据具体问题灵活应用。 在结构设计方面,该书详细解释了模拟与预测的流程,从初始假设到参数优化,再到验证与调整,逻辑清晰、步骤系统。这部分章节对读者非常实用,特别适合技术人员和研究生在实际项目中进行应用。同时,书中也充满了丰富的图表、案例分析和实践指南,使得理论知识更易于理解和转化。 内容的深度体现在对不确定性管理的重要讨论上,该书不仅关注模型的准确性,还强调如何量化和控制不确定因素,提升研究结果的可靠性。这一部分提供了多种处理不确定性的策略,如敏感性分析和风险评估,为读者开拓了应对复杂问题的新思路。 书中还特别关注跨学科的融合,结合工程、经济学等多领域的观点,展示了模型在不同背景下的应用价值。这种综合性思维,使得读者能够从多个角度理解和解释技术内容,提升其综合分析能力。此外,通过大量引用最新研究成果和行业案例,本书为读者提供了实证支持,增强了理论与实践的结合力。 总体而言,这本书以清晰、系统的结构和丰富的内容,为读者构建了一套完整的过程技术分析方法体系,既有理论深度,也富有实际应用价值。这些方面使得它成为研究者和从业者必备的参考资料。 这个书的设计目标在于帮助读者掌握系统化的思维方式,通过科学的方法论来应对复杂的技术挑战,不仅提升了他们分析能力,还为未来的深入研究奠定了坚实基础。阅读过程中,人们可以期待获得全面而深刻的知识框架,从而在实际工作中灵活运用这些理念,推动技术进步。

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