Tuning Inputs to Maintain Profits


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Soil and Water Quality


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Tune-up on Corporate Tax Issues


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Sustainable Textiles


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Environmental issues are playing an increasingly important role in the textile industry, both from the point of view of government regulation and consumer expectations. Sustainable textiles reviews ways of achieving more sustainable materials and technologies as well as improving recycling in the industry.The first part of the book discusses ways of improving sustainability at various points in the supply chain. Chapters discuss how sustainability can be integrated into textile design, ensuring more sustainable production of both natural and synthetic fibres, improving sustainability in processes such as dyeing as well as more environmentally-friendly technologies including enzyme and plasma technologies. The second part of the book reviews consumer perceptions of recycled textiles, eco-labelling, organic textiles and the use of recycled materials in textile products.With a distinguished editor and an impressive range of international contributors, Sustainable textiles is an important reference for the textile industry and those researching this important topic. - Reviews government regulations and consumer expectations about environmental impact on the textiles industry - Discusses ways of achieving more sustainable materials and technologies as well as textiles recycling - Examines how sustainability can be integrated into textile design, production and processes




Proceedings


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Multivariable Predictive Control


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A guide to all practical aspects of building, implementing, managing, and maintaining MPC applications in industrial plants Multivariable Predictive Control: Applications in Industry provides engineers with a thorough understanding of all practical aspects of multivariate predictive control (MPC) applications, as well as expert guidance on how to derive maximum benefit from those systems. Short on theory and long on step-by-step information, it covers everything plant process engineers and control engineers need to know about building, deploying, and managing MPC applications in their companies. MPC has more than proven itself to be one the most important tools for optimising plant operations on an ongoing basis. Companies, worldwide, across a range of industries are successfully using MPC systems to optimise materials and utility consumption, reduce waste, minimise pollution, and maximise production. Unfortunately, due in part to the lack of practical references, plant engineers are often at a loss as to how to manage and maintain MPC systems once the applications have been installed and the consultants and vendors’ reps have left the plant. Written by a chemical engineer with two decades of experience in operations and technical services at petrochemical companies, this book fills that regrettable gap in the professional literature. Provides a cost-benefit analysis of typical MPC projects and reviews commercially available MPC software packages Details software implementation steps, as well as techniques for successfully evaluating and monitoring software performance once it has been installed Features case studies and real-world examples from industries, worldwide, illustrating the advantages and common pitfalls of MPC systems Describes MPC application failures in an array of companies, exposes the root causes of those failures, and offers proven safeguards and corrective measures for avoiding similar failures Multivariable Predictive Control: Applications in Industry is an indispensable resource for plant process engineers and control engineers working in chemical plants, petrochemical companies, and oil refineries in which MPC systems already are operational, or where MPC implementations are being considering.




Deep learning for computer vision in the art domain


Book Description

In recent years, computer vision algorithms based on machine learning have seen rapid development. In the past, research mostly focused on solving computer vision problems such as image classification or object detection on images displaying natural scenes. Nowadays other fields such as the field of cultural heritage, where an abundance of data is available, also get into the focus of research. In the line of current research endeavours, we collaborated with the Getty Research Institute which provided us with a challenging dataset, containing images of paintings and drawings. In this technical report, we present the results of the seminar "Deep Learning for Computer Vision". In this seminar, students of the Hasso Plattner Institute evaluated state-of-the-art approaches for image classification, object detection and image recognition on the dataset of the Getty Research Institute. The main challenge when applying modern computer vision methods to the available data is the availability of annotated training data, as the dataset provided by the Getty Research Institute does not contain a sufficient amount of annotated samples for the training of deep neural networks. However, throughout the report we show that it is possible to achieve satisfying to very good results, when using further publicly available datasets, such as the WikiArt dataset, for the training of machine learning models.