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Table of contents

Vapor flow lag in distillation columns, Chem. HOCK, B. DMC control of a complex refrigerated fractionator, Adv. Control, 44, Improvements in dynamic compartmental modelling for distillation, Comput. Unsteady state behavior of multicomponent distillation columns: part I: simulation, AlChE Journal, 16, Studies on dynamics and control of distillation columns, Ph.


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  7. A fractional order fuzzy PID controller for binary distillation column control.

Control of Unstable Distillation Columns, Proc. Published in: Puigjaner and Espuna. Eds, Computer oriented Process Engineering, Elsevier, Robust control of homogenous azeotropic distillation columns, AlChE Journal, 37, 12, Modelling and identification for robust control of ill-conditioned plants - a distillation case study, Proc.

Computerized control scheme development of distillation columns using multiple temperature inputs systems, ISA Trans. Multi-temps give better control, Hydrocarbon Processing, TONG, M. An analytical approach to approximate dynamic modelling of distillation towers, Ind.

Effect recycle structure on distillation tower time constants, AlChE Journal, 32, Modelling of packed bed distillation column, Ph. Thesis, Chalmers Univ. PARK, H. Two-part modular reduced-order model for multicomponent multistage distillation columns, Journal of chemical Eng. KIM, J. Adaptive control of a binary distillation column using quadratic programming, Korean J. Steady-state analysis of interaction between pressure and temperature or composition loops in a single distillation tower, Ind.

Simple model for dynamic simulation of stage separation processes with very volatile components, AIChE Journal, 32, doi Heterogenous azeotropic distillation - homotopy continuation methods, Comput. Controller adjustment for improved nominal performance and robustness - II: Robust geometric control of a distillation column, Chem.

Sci, 42, doi Geometric analysis of the global stability of linear inverse-based controllers for bivariate nonlinear processes, Ind. Numerical studies in solving dynamic distillation problems, Comput. A case-study of multivariable control for a multicomponent distillation unit on a pilot plant scale, Proc. American Control Conference, Distillation with an intermittent sidewithdrawal to control medium boiling impurities in the feed, Presented at American Control Conference, Boston. Optimising control for an industrial distillation column using a simplified model, R.

APII, Argentina, 23, PARK, S. Dynamic structural transformation for the analysis of distillation control structures, AIChE Journal, 37, doi Quality control of binary distillation columns via nonlinear aggregated models, Automatica, 27, doi Response modes of a binary distillation column, Ind Eng Chem.

Fundamentals, 8, doi Computing multiple solutions to systems of interlinked separation columns, AIChE, Journal, 33, doi Distillation tray fundamentals, Cambridge University Press. Control of distillation columns with sharp temperature profiles, AlChE Journal, 17, Parallel cascade control, Ind. Steady-state energy conservation aspects of distillation column control system design, Ind. Constraint control on distillation columns, Automatica, 6, doi WANG, Y.

Survey of recent distillation control results, ISA Trans. A dynamic comparison of material balance versus conventional control of distillation columns, Proc. Application of dynamic matrix control to moderate- and high-purity distillation towers, Ind. Impact of model uncertainty descriptions for high-purity distillation control, AIChE Journal, 34, TAN, G. Estimation of distillation composition from multiple temperature measurements using PLS regression, Ind. Composition estimator in a pilot plant distillation column using multiple temperatures, Ind Eng. How construction affects column control, Hydrocarbon Processing, 66, Selecting sensor location and type for multivariable processes, In: Prett, D.

Advanced controls improve performance of two-shell, dualpressure column, Oil and Gas Journal, 88, Control of vapor recompression distillation columns, Ind. TANI, S. Crude unit product quality control, Comput. Adchem, Lyngby, Denmark, Distillation columns, Instrument Society of America. Control of a binary sidestream distillation column, Ind.


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  • Nonlinear predictive control of packed distillation column, Proc. Order reduction strategies for models of staged separation systems, Comput. Dynamics and control of continuous distillation columns, Elsevier. Identification of distillation process dynamics comparing process knowledge and black box based approaches, Proc.

    American Control Conference, San Diego, A model-based control system for a distillation column, Chem. Distinctive problems of process control, Chem. The control of distillation columns, Trans. A Lyapunov function with applications to some nonlinear physical systems, Automatica, 1, doi The transient behaviour of distillation columns and heat exchangers: An historical and critical review, Trans.

    Instn, Chem.

    Distillation Column Control

    Dynamics of heterogeneous azeotropic distillation columns, AlChE Journal, 36, Rigorous dynamics and control of continuous distillation systems - Simulation and experimental result, Comput. Rigorous dynamics and feedforward control design for distillation processes, AlChE Journal, 36, Operability and control of azeotropic heterogeneous distillation sequences, Proc. Systems Engineering. GANI, R. Simulation and design of distillation columns part I: Hydraulic model and dynamic behavior, Lat. New strategy improves dual composition column control, Hydrocarbon Process, 60, Explicit versus implicit decoupling in distillation control, In: Seborg and Edgar.

    Disturbance rejection properties of control structures at one-point control of a two-product distillation column, Ind. Robust multiobjective linear quadratic control of distillation using low-order controllers, Chem. Controllers design for a multi-product distillation unit in the presence of uncertainty, Distillation and Absorbtion 87, Brighton, UK. Nonlinear analysis in process design: Why overdesign to avoid complex nonlinearities, Ind.

    Nonlinear analysis in process design, AlChE Journal, 37, Multivariate control of industrial fractionators, Application of multivariable control in an integrated control system. MAH, R. Should a specialist supplier be used? If an inferential proves infeasible, what additional measurements should be installed? Delaying answering these questions until after the APC contract is awarded jeopardises benefit capture. The myths perpetuated by the suppliers of both regression-based inferentials including artificial neural networks and first-principle types are described.

    A reasoned approach is presented as to which technology should be chosen for each case and how external suppliers might be involved. The principles of OLS ordinary least squares regression analysis are explained.

    Distillation PID Control in Simulink (MATLAB)

    This includes the choice of penalty function minimised by regression and the use of Pearson R to assess the accuracy of the resulting correlation. The student is presented with a case study aimed to assess different penalty functions and to identify the limitations of Pearson R.

    This is followed by a second case study in which the advantages of using the adjusted version of Pearson R in assessing how many sets of historical data are required and how many inputs should be used. Issues concerned with the quality of the input data are then addressed, supported by a range of student exercises. This includes how the level of 'scatter' impacts the level of confidence in the resulting correlations, problems associated with data not being collected under steady-state conditions and the importance of accurately time-stamping of measured property.

    Students are given the opportunity to develop dynamically compensated inferentials.

    Loop-Shaping Design - MATLAB & Simulink - MathWorks Deutschland

    An effective performance index is then described. Students learn through further case studies how it can be used to assess whether a new inferential is sufficiently accurate, how the index can be incorporated into ongoing monitoring and how it helps is assessing the benefit captured.

    A number of real case studies are then presented demonstrating how process engineering knowledge should be included in regressed inferentials. Examples included the derivation of linear and non-linear pressure compensated temperatures for use in the control of distillation columns.

    Distillation Control, Optimization, and Tuning: Fundamentals and Strategies

    This is extended to the use of multiple tray temperatures. The use of WLS weighted least squares regression is described as a means of dealing with suspect measurements. Other case studies show how changes in operating mode or feed type can be incorporated. Automatic bias updating is covered, using a reactor-based case study which is then extended to show how weighted temperatures, space velocity and catalyst activity can be incorporated to make updating redundant.

    Distillation control, optimization, and tuning : fundamentals and strategies

    Perhaps more than any other engineering discipline, process control engineers make extensive use of statistical methods. Embedded in proprietary control design and monitoring software, the engineer may not even be aware of them. The purpose of this module is to draw attention to the importance of statistics throughout all stages of implementation of improved controls — from estimation of the economic benefits, throughout the design phase, ongoing performance monitoring and fault diagnosis. The module starts by explaining the central tendency of data.

    In particular it addresses the importance of accurately determining the mean, since the forms the basis of many statistical calculations. For example, following implementation of a control improvement, small errors in the estimate of before and after values will result in a major error in estimating the improvement. Further, any error in the estimate of the mean will result in overestimating parameters such as standard deviation. In addition to the conventional arithmetic mean, uses of other versions such as the harmonic mean, geometric mean and logarithmic mean are described in detail.