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This volume contains 40 papers which describe the recent developments in advanced control of chemical processes and related industries. The topics of adaptive control, model-based control and neural networks are covered by 3 survey papers. New adaptive, statistical, model-based control and artificial intelligence techniques and their applications are detailed in several papers. The problem of implementation of control algorithms on a digital computer is also considered.
In addition to the three main themes: chemical reactors, distillation columns, and batch processes this volume also addresses some of the new trends in dynamics and control methodology such as model based predictive control, new methods for identification of dynamic models, nonlinear control theory and the application of neural networks to identification and control. Provides a useful reference source of the major advances in the field.
This publication brings together the latest research findings in the key area of chemical process control; including dynamic modelling and simulation - modelling and model validation for application in linear and nonlinear model-based control: nonlinear model-based predictive control and optimization - to facilitate constrained real-time optimization of chemical processes; statistical control techniques - major developments in the statistical interpretation of measured data to guide future research; knowledge-based v model-based control - the integration of theoretical aspects of control and optimization theory with more recent developments in artificial intelligence and computer science.
As with all of pharmaceutical production, the regulatory environment for the production of therapeutics has been changing as a direct result of the US FDA-initiated Quality by Design (QbD) guidelines and corresponding activities of the International Committee for Harmonization (ICH). Given the rapid growth in the biopharmaceutical area and the comp
The existence of interactions between the design of a process and that of its control system have been known to industrial practitioners for a long time. In the past decade academic research has produced methodologies and tools that begin to address the issue of designing processes that are flexible, can be controlled reliably, and are inherently safe. This publication unites the work of academics and practitioners with interests in the integration of process design and control, in order to examine the state of the art in methodologies and applications. The scope covers the design of chemical plants at different stages of detail. It also examines control issues from the plantwide level, where, for example, recycles between units can be important, to the specific unit level, where the availability or selection of measurements might be the most important factor.
Proceedings of the European Control Conference 1991, July 2-5, 1991, Grenoble, France
Artificial neural networks and genetic algorithms both are areas of research which have their origins in mathematical models constructed in order to gain understanding of important natural processes. By focussing on the process models rather than the processes themselves, significant new computational techniques have evolved which have found application in a large number of diverse fields. This diversity is reflected in the topics which are subjects of the contributions to this volume. There are contributions reporting successful applications of the technology to the solution of industrial/commercial problems. This may well reflect the maturity of the technology, notably in the sense that 'real' users of modelling/prediction techniques are prepared to accept neural networks as a valid paradigm. Theoretical issues also receive attention, notably in connection with the radial basis function neural network. Contributions in the field of genetic algorithms reflect the wide range of current applications, including, for example, portfolio selection, filter design, frequency assignment, tuning of nonlinear PID controllers. These techniques are also used extensively for combinatorial optimisation problems.
This volume explores the application of Quality by Design (QbD) to biopharmaceutical drug product development. Twenty-eight comprehensive chapters cover dosage forms, liquid and lyophilized drug products. The introductory chapters of this book define key elements of QbD and examine how these elements are integrated into drug product development. These chapters also discuss lessons learned from the FDA Office of Biotechnology Products pilot program. Following chapters demonstrate how QbD is used for formulation development ranging from screening of formulations to developability assessment to development of lyophilized and liquid formats. The next few chapters study the use of small-scale and surrogate models as well as QbD application to drug product processes such as drug substance freezing and thawing, mixing, sterile filtration, filling, lyophilization, inspection and shipping and handling. Later chapters describe more specialized applications of QbD in the drug product realm. This includes the use of QbD in primary containers, devices and combination product development. The volume also explores QbD applied to vaccine development, automation, mathematical modeling and monitoring, and controlling processes and defining control strategies. It concludes with a discussion on the application of QbD to drug product technology transfer as well as overall regulatory considerations and lifecycle management. Quality by Design for Biopharmaceutical Drug Product Development is an authoritative resource for scientists and researchers interested in expanding their knowledge on QbD principles and uses in creating better drugs.