Showing papers in "Computers & Chemical Engineering in 2004"

TL;DR: This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty and discusses and contrast the classical recourse-based stochastic programming, robust stochastics programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastically dynamic programming.

TL;DR: An overview of developments in the scheduling of multiproduct/multipurpose batch and continuous processes is presented and various continuous-time models have been proposed in the literature and their strengths and limitations are examined.

TL;DR: A general classification of mathematical optimization problems is provided, followed by a matrix of applications that shows the areas in which these problems have been typically applied in process systems engineering.

TL;DR: With key variables selected by genetic algorithms and the contribution charts, SVM and PSVM outperformed FDA and demonstrated the advantage of using nonlinear technique when data are overlapped and the effectiveness of the proposed approach is increased in PSVM.

TL;DR: In the paper, a systematic procedure for control structure design for complete chemical plants (plantwide control) is presented, and starts with carefully defining the operational and economic objectives, and the degrees of freedom available to fulfill them.

TL;DR: This paper will consider important issues in supply chain design and operation drawn from the literature and from the authors' collaborative research projects in this area.

TL;DR: The proposed data filter-cleaner includes an on-li ne outlier-resistant estimate of the process model and combines it with a modified Kalman filter to detect and “clean” outliers.

TL;DR: A two-phase fuzzy decision-making method is presented and by means of application of it to a numerical example, proved effective in providing a compromised solution in an uncertain multi-echelon supply chain network.

TL;DR: A novel robust optimization methodology is proposed, which when applied to mixed-integer linear programming (MILP) problems produces “robust” solutions which are in a sense immune against bounded uncertainty.

TL;DR: The problem of determining the safety stock level to use to meet a desired level of customer satisfaction is addressed using a simulation based optimization approach and an industrial-scale case problem is presented to demonstrate the utility of the proposed approach.

TL;DR: Support vector machine (SVM), a new powerful machine learning method based on statistical learning theory (SLT), is introduced into soft sensor modeling and a model selection method within the Bayesian evidence framework is proposed to select an optimal model for a soft sensor based on SVM.

TL;DR: The challenges and opportunities are highlighted in terms of the needs for multi-level modeling with emphasis on property models that are suitable for computer-aided applications, flexible solution strategies that are able to solve a large range of chemical product design problems and finally, a systemschemical product design framework with the overall objective to reduce the time and cost to market a new or improved product.

TL;DR: The proposed batch monitoring method using multiway kernel principal component analysis (MKPCA) can effectively capture the nonlinear relationships among process variables in both off-line analysis and on-line batch monitoring.

TL;DR: The focus of the present work is to propose a general framework for modeling petroleum supply chains, which is a large-scale MINLP and results show model performance by analyzing different scenarios.

TL;DR: This work considers the optimal investment and operational planning of gas field developments under uncertainty in gas reserves and presents a novel stochastic programming model that incorporates the decision-dependence of the scenario tree.

TL;DR: This work first discusses recent advances towards the integration of process design, process control and process operability from the open literature and then focuses on techniques towards this endeavor that were developed within the group at Imperial College.

TL;DR: An overview of the financial state of the process industry, major issues it currently faces, and job placement of chemical engineers in the US is provided, and a brief review of the progress that has been made in these areas is provided.

TL;DR: The concept of conventional PCA is expanded such that a Gaussian mixture model is used to approximate the data pattern in the model subspace obtained by PCA, and a GMM via discriminant analysis is proposed to isolate faults.

TL;DR: Methods based on the Cauchy distribution and Hampel’s redescending M-estimator give promising results for data reconciliation and gross error detection with less computation.

TL;DR: This work discusses recent developments in deterministic global optimization algorithms applied to both nonlinear programs and mixed-integer programs, and discusses logic-based optimization and its influence in both modeling and solving mixed- integer optimization problems.

TL;DR: This work compared the performance of a new technique, the unscented filter, with that of the extended Kalman filter, and found the unscenceed filter produced better results without performing potentially ill-conditioned numerical calculations and linearly approximating the evolution of the state vector covariance.

TL;DR: The impact over the past 40 years of process systems thinking on the design of chemical processes is discussed, noting that this progress is inextricably linked with the development of computer technology.

TL;DR: A novel MILP continuous mathematical formulation that neither uses time discretization nor division of the pipeline into a number of single-product packs is presented, resulting in a severe reduction in binary variables and CPU time with regards to previous approaches.

TL;DR: This paper presents a systematic mathematical programming approach for long-term, multi-site capacity planning under uncertainty in the pharmaceutical industry and proposes a hierarchical algorithm in order to reduce the computational effort needed for the solution of the resulting large-scale MILP problem.

TL;DR: This paper addresses bilevel decision-making problems under uncertainty in the context of enterprise-wide supply chain optimization with one level corresponding to a plant planning problem, while the other to a distribution network problem with an effective solution strategy based on parametric programming techniques.

TL;DR: A novel superstructure optimization approach for the design of distillation sequences of zeotropic mixtures using the Underwood–Fenske–Gilliland approximation and is formulated as a generalized disjunctive programming problem, which has proven to be very robust and reliable.

TL;DR: External analysis is proposed to distinguish faults from normal changes in operating conditions and to further improve the monitoring performance, a new MSPC method based on independent component analysis (ICA) is used.

TL;DR: The simultaneous use of all algorithms is suggested to obtain a wider spectrum of efficient solutions and the quality and diversification of Pareto-optimal solutions generated by all algorithms are found to be problem specific.

TL;DR: This work decomposes the overall problem spatially into three domains: the crude-oil unloading and blending, the production unit operations and the product blending and delivery, and proposes a comprehensive mathematical programming model for the efficient scheduling of oil-refinery operations.

TL;DR: A decision support system to investigate and improve the combined inventory and transportation system in a representative world-wide crude supply problem and proposes an approximation architecture as a potential solution strategy attempting to solve the optimal control problem.