Showing papers in "Computers & Chemical Engineering in 2014"

TL;DR: In this article, the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains are described, along with a comprehensive overview and classification of the existing contributions on biofuel/bio-energy supply chain optimization.

TL;DR: The aim of the paper is to focus on the industrial aspects of scheduling and discuss the main characteristics, including strengths and weaknesses of the presented approaches, as well as some lessons learned from industry.

TL;DR: It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

TL;DR: This paper identifies key elements of control and scheduling, and carries out a systematic investigation of their use as building blocks for the formulation and solution of the integrated scheduling/control problem.

TL;DR: Results of the case study, optimization of carbon dioxide capture process with aqueous amines, revealed that the mixed adaptive sampling algorithm may reduce the required sample size by up to 40% compared to a purely space-filling design.

TL;DR: This paper describes nonlinear methods in model building, dynamic data reconciliation, and dynamic optimization that are inspired by researchers and motivated by industrial applications and a new formulation of the l1-norm objective with a dead-band for estimation and control.

TL;DR: A design and planning approach is proposed for addressing general multi-period, multi-product closed-loop supply chains (CLSCs), structured as a 10-layer network, with uncertain levels in the amount of raw material supplies and customer demands.

TL;DR: A systematic procedure to reduce a complete data set of the energy demand profiles into a limited number of typical periods, which adequately preserve significant characteristics of the yearly profiles is presented.

TL;DR: This contribution addresses the multi-period synthesis of an optimally integrated regional biomass and bioenergy supply network through a mixed-integer linear programing (MILP) approach to maximize the sustainably viable utilization of resources by accounting for the competition between fuels and food production.

TL;DR: This review critically examines the dynamic PCC process models developed to date and analyses the different approaches used, as well as the model complexity and their limitations, and key areas that need to be addressed in future dynamic models.

TL;DR: In this paper, a bilevel mixed-integer nonlinear programming (MINLP) model is proposed for the optimal design and planning of non-cooperative supply chains from the manufacturer's perspective.

TL;DR: A rigorous multi-scale optimization framework is developed that substitutes RMs for complex original detailed models (ODMs) and guarantees convergence to the original optimization problem and leads to three related NLP algorithms for RM-based optimization.

TL;DR: A historical perspective and an overview of the pioneering work that Manfred Morari developed in the area of resiliency for chemical processes are provided and some of the recent developments that have taken place since that early work are provided.

TL;DR: A novel algorithm to determine Pareto sets in a state-of-the-art steady-state flow sheet simulator is integrated and an approximation of predefined accuracy of the Pare to set is calculated.

TL;DR: In this article, a mixed-integer linear programming model is proposed to simultaneously consider the supply chain design, integration strategy selection, and production planning, and a robust optimization approach which tradeoffs the performance and conservatism is adopted to deal with the demand and supply uncertainty.

TL;DR: It is demonstrated that incorporating measured variables that do not provide any additional information about faults degrades monitoring performance, and a stochastic optimization-based method is proposed to identify an optimal subset of measured variables for process monitoring.

TL;DR: To further improve the predictive accuracy of the MJIT-KPLS algorithm, a hybrid similarity measure and an adaptive selection strategy are proposed for selecting local modeling samples and maximal similarity replacement rule is proposed to update database.

TL;DR: A systematic framework for the onsite design and implementation of the control system in continuous tablet manufacturing process has been developed and has different novel features such as the option to run the plant in closed-loop (MPC/PID), open-loop and simulation mode.

TL;DR: This research presents a new probabilistic method to estimate the polymethine content of Na6(CO3)(SO4) through the H2O2/O2 mixture and finds it to be a good mixture for high-performance liquid chromatographyatography.

TL;DR: An MILP formulation that integrates the operational and strategic decision-making for continuous power-intensive processes under time-sensitive electricity prices is proposed and the trade-off between capital and operating expenditures is demonstrated.

TL;DR: A multi-objective optimization model for sizing and operation optimization of district heating systems with heat storage tanks is presented, which includes process design and energy integration techniques for optimizing the temperature intervals, the volume and the operation strategy of thermal storage tanks.

TL;DR: The systematic methodology for design of tailor-made blended products has been developed and is highlighted through two case studies involving gasoline blends and lubricant base oils.

TL;DR: It is demonstrated that the use of risk metrics can help operators to systematically mitigate system volatility and is performed a degrees-of-freedom analysis to assess operational flexibility and to determine conditions for model consistency.

TL;DR: A novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall is introduced.

TL;DR: This paper is devoted to an extensive review of mathematical programming models in this context, based on a taxonomy framework, and investigates ongoing and emerging challenges surrounding the strategic and tactical decisions of COSC problems.

TL;DR: An optimization framework is proposed with the aim of achieving a feasible and stable optimal process design in the presence of stochastic disturbances while using advanced model-based control schemes such as Model Predictive Control (MPC).

TL;DR: This classification fundamentally differentiates between “projecting methods”, where controllability is monitored during the process design to predict the trade-offs between design and control, and the “integrated-optimization methods’ which solve the processDesign and the control-systems design at once within an optimization framework.

TL;DR: A new multiobjective stage cost is proposed that captures economics as well as risk at a node, using a weighted sum of an economic cost and a tracking stage cost for supply chain optimization.

TL;DR: A benchmarking of different hierarchical control structures for WWTPs that combines static and dynamic real time optimization (RTO) and nonlinear model predictive control (NMPC) is presented to evaluate the enhancement of the operation in terms of economics and effluent quality.

TL;DR: A novel scenario reduction algorithm is proposed in this paper to incorporate the consideration of both input data and output performance of decision making and leads to reduced distribution not only closer to the original distribution in terms of the transportation distance, but also captures the performance of the output.