Computational Intelligence Techniques For Bioprocess Modelling Supervision And Control

Computational Intelligence (CI) and Bioprocess are well-established research areas which have much to offer each other. Under the perspective of the CI area, Biop- cess can be considered a vast application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to boosting the development of new intelligent techniques as well as to help the refinement and s- cialization of many of the already existing techniques. Under the perspective of the Bioprocess area, CI can be considered a useful repertoire of theories, methods and techniques that can contribute and offer interesting alternative approaches for solving many of its problems, particularly those hard to solve using conventional techniques. Although throughout the past years CI and Bioprocess areas have accumulated substantial specific knowledge and progress has been quick and with a high degree of success, we believe there is still a long way to go in order to use the potentialities of the available CI techniques and knowledge at their full extent, as tools for supporting problem solving in bioprocesses. One of the reasons is the fact that both areas have progressed steadily and have been continuously accumulating and refining specific knowledge; another reason is the high level of technical expertise demanded by each of them. The acquisition of technical skills, experience and good insights in either of the two areas is very demanding and a hard task to be accomplished by any professional.

This is the most comprehensive treatise of this topic available, providing invaluable information on the technological and economic benefits to be gained from implementing continuous processes in the biopharmaceutical industry. Top experts from industry and academia cover the latest technical developments in the field, describing the use of single-use technologies alongside perfusion production platforms and downstream operations. Special emphasis is given to process control and monitoring, including such topics as 'quality by design' and automation. The book is supplemented by case studies that highlight the enormous potential of continuous manufacturing for biopharmaceutical production facilities.

The 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering, contains the papers presented at the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event. It is a valuable resource for chemical engineers, chemical process engineers, researchers in industry and academia, students, and consultants for chemical industries. Presents findings and discussions from the 34th European Symposium on Computer Aided Process Engineering / 15th International Symposium on Process Systems Engineering joint event

Data Management is the process of planning, coordinating and controlling data resources. More often, applications need to store and search a large amount of data. Managing Data has been continuously challenged by demands from various areas and applications and has evolved in parallel with advances in hardware and computing techniques. This volume focuses on its recent advances and it is composed of five parts and a total of eighteen chapters. The first part of the book contains five contributions in the area of information retrieval and Web intelligence: a novel approach to solving index selection problem, integrated retrieval from Web of documents and data, bipolarity in database querying, deriving data summarization through ontologies, and granular computing for Web intelligence. The second part of the book contains four contributions in knowledge discovery area. Its third part contains three contributions in information integration and data security area. The remaining two parts of the book contain six contributions in the area of intelligent agents and applications of data management in medical domain.

This book explores the increasing convergence of Social Media and Semantic Web technologies. It offers up-to-date contributions that illustrate various approaches to this young and emerging technology area.

A history of of the industrial ecosystem that focuses on the biological sewage treatment plant as an early example. Biological sewage treatment, like electricity, power generation, telephones, and mass transit, has been a key technology and a major part of the urban infrastructure since the late nineteenth century. But sewage treatment plants are not only a ubiquitous component of the modern city, they are also ecosystems--a hybrid variety that incorporates elements of both nature and industry and embodies multiple contradictions. In Hybrid Nature, Daniel Schneider offers an environmental history of the biological sewage treatment plant in the United States and England, viewing it as an early and influential example of an industrial ecosystem. The sewage treatment plant relies on microorganisms and other plants and animals but differs from a natural ecosystem in the extent of human intervention in its creation and management. Schneider explores the relationship between society and nature in the industrial ecosystem and the contradictions that define it: the naturalization of industry versus the industrialization of nature; the public interest versus private (patented) technology; engineers versus bacterial and human labor; and purification versus profits in the marketing of sewage fertilizer. Schneider also describes biotechnology's direct connections to the history of sewage treatment, and how genetic engineering is extending the reaches of the industrial ecosystem to such "natural" ecosystems as oceans, rivers, and forests. In a conclusion that shows how industrial ecosystems continue to evolve, Schneider discusses John Todd's Living Machine, a natural purification method of sewage treatment, as the embodiment of the contradictions of the industrial ecosystem.

The overwhelming pace of evolution in technology has made it possible to develop intelligent systems which help users in their dayly life activities. - cordingly, methods of recording, managing and analysing data have evolved from the very simple ?le systems into complex ambient supportive intelligent systems. This book arises as a compilation of methods, techniques and tools c- nected with data related issues: from modelling to analysis. A broad range of approaches such as database self-* techniques for ubiquitous environments, multimedia data, or data driven models will be reviewed. Di?erent areas of applications, in which data models conceptualize nowadays reality, starting from e-learning to electric transformers will be considered. The book is a collection of representative contributions to cover the sp- trum related to data bases, which support decision making and data mining methods as well as conceptualization. Datawarehouse technology and m- eling are presented in the ?rst chapter together with the deep review of datawarehouse techniques for supporting e-learning processes with special emphasis on data cubes, all the tools are considered in the context of imp- mentationofsoftwareapplication.Thesecondchaptercontinueswiththes- ilar technology and deals with the community data warehouse architecture.

The extremely rapid progress of science dealing with the design of new computer systems and the development of intelligent algorithmic solutions for solving c- plex problems has become apparent also in the field of computational intelligence and cognitive informatics methods. The progress of these new branches of inf- matics has only started a few years ago, but they are already making a very s- nificant contribution to the development of modern technologies, and also forming the foundations for future research on building an artificial brain and systems i- tating human thought processes. We are already able to build robots with basic machine intelligence, which can sometimes perform complex actions and also - erate by adapting to changing conditions of their surroundings. This very impr- sive development of intelligent systems is manifested in the creation of robotic devices which use artificial intelligence algorithms in their operations, mo- ments, when solving difficult problems or communicating with humans. It is also evidenced by the introduction of new methods of reasoning about and interpreting objects or events surrounding the system. One of the fields in which the need to deploy such modern solutions is obvious are cognitive vision systems used both in mobile robots and in computer systems which recognise or interpret the meaning of recorded signals or patterns.