Automated affinity separation processes

This paper describes the automation of fixed-bed affinity separation techniques for the purification of proteins. The basic requirements of an automated system suitable for carrying out such a separation are detailed together with an introduction to the concept of process optimisation by interactive control. Such systems can continue to optimise separations when operating conditions are variable but require that methods for the on-line analysis of separation performance are available. Such methods require that the levels of particular proteins in the presence of other proteins can be determined and include continuous measurement of enzyme activity and the use of the new technique of rapid chromatographic analysis. The latter technique, which can also be used more generally in the process control of protein purification, is described and its use illustrated.     

Dynamic simulation and optimisation of tubular polymerisation reactors in gPROMS

In this paper a dynamic model of the high-pressure polymerisation of ethylene in tubular reactors is introduced and a dynamic optimisation problem is formulated for studying start-up strategies. The optimisation objectives proposed are to maximise outlet conversion and optimise the time necessary for it's stabilisation while keeping product molecular properties between commercial ranges. Results are shown giving the time responses for temperature, number-average molecular weight and conversion along the reactor axial distance, which were obtained using different control variable profiles. Improving in reactor productivity is achieved. The interface gOPT of the gPROMS simulator was used to resolve the optimisation problem and to perform the simulations.     

Colour recipe prediction using ant colony algorithm: principle of resolution and analysis of performances

This paper presents a new method for colour recipe prediction using ant colony optimisation. Three reactive dyes, namely CI Reactive Yellow 145, CI Reactive Red 238 and CI Reactive Blue 235, were used for colour formulation. Samples of 100% cotton fabrics were used for dyeing. The objective was to assure, control and optimise the colour formulation step by determining the dyes to be applied and their respective concentrations to reproduce the desired shades. The criterion of optimisation is to minimise the colour differences [Colour Measurement Committee (2:1)] between the target colour and the colour obtained by the proposed recipe. Errors between the proposed recipe and actual concentrations are also evaluated. The developed algorithm showed good performances with small colour differences between the target and reproduced colours (all lower than 0.7).     

A new method for calculating and correcting molecular weight distributions from gel permeation chromatography

A new method for calculating and correcting molecular weight distributions of polymer samples from GPC chromatograms is presented. The integral equation which relates the true molecular weight distribution of polymer sample to the chromatogram is reformulated into an equivalent variational problem of quadratic functional. The method of steepest descent in the function space is then applied to the minimization problem to obtain the true molecular weight distribution. This method is efficient and reduces some of the oscillation problems encountered in the previous methods. Examples are given.     

Simulation of the consolidation of paper coating structures: probabilistic versus deterministic models

Two categories of mathematical models were compared for the simulation of consolidation of paper coating structures, that is for the packing of pigments on a paper substrate under dewatering conditions. The first category uses probabilistic methods, relying on a random number generator to either determine the initial position of the pigments or their motion. The second category uses deterministic methods based on force balances. In this work, two probabilistic models and two deterministic models are described and their respective advantages and drawbacks are critically reviewed. Simulation results obtained using three of these methods are compared for the case of monodisperse and bidisperse spherical suspensions. Porosity calculations of the numerical packings obtained with the (deterministic) discrete element method (DEM) and two probabilistic methods, the Monte-Carlo (MCD) and the steepest descent (SDD) deposition methods, are compared with experimental data from the literature. These calculations reveal significant differences in the pore volume obtained with these three models. An analysis based on the bridging and relaxation phenomena that prevail in the flow of such particulate systems provide an explanation for these differences and show the strong potential of the discrete element method.

The choice of the simulation method depends on the objective of the simulations. DEM will provide more accurate predictions of macroscopic quantities such as the porosity or the roughness, but requires very long computational times. MCD or SDD will only provide qualitative trends, but is computationally far less intense. A combination of strategies might be appropriate, using MCD (or SDD) to provide guidelines and DEM to enhance the results predicted by MCD.     

Essentially exact ground-state calculations by superpositions of nonorthogonal Slater determinants

An essentially exact ground-state calculation algorithm for few-electron systems based on superposition of nonorthogonal Slater determinants (SDs) is described, and its convergence properties to ground states are examined. A linear combination of SDs is adopted as many-electron wave functions, and all one-electron wave functions are updated by employing linearly independent multiple correction vectors on the basis of the variational principle. The improvement of the convergence performance to the ground state given by the multi-direction search is shown through comparisons with the conventional steepest descent method. The accuracy and applicability of the proposed scheme are also demonstrated by calculations of the potential energy curves of few-electron molecular systems, compared with the conventional quantum chemistry calculation techniques.     

Solving an Inverse Problem of Erosive Burning Rate Reconstruction

A new method for an experimental study of burning processes in condensed substances is suggested, based on the statement and solution of inverse problems. An inverse problem of reconstructing the erosive burning rate of solid propellants from experimental data is formulated. The choice of an approach to solving the problem by the joint application of well-known methods for inverse problem solution and specific features of experimental studies of burning processes, in particular, erosive burning, has been justified. The problem solution is illustrated by a numerical example. The testing involves a comparative analysis of two optimization methods: although both methods are characterized by an identical accuracy, the steepest descent method has a higher rate of convergence for this class of problems than the conjugate gradient method.     

Optimal operating strategies for emulsion terpolymerisation

Optimal control policies for emulsion terpolymerisation of styrene, methyl methacrylate (MMA) and methyl acrylate (MA) were determined in a semi-batch reactor using the multi-objective dynamic optimisation method. A comprehensive dynamic model was used for the design of optimal control. The control vector parameterisation (CVP) approach was implemented for constrained optimisation for emulsion terpolymerisation reactors. The feed rates of styrene, MMA, MA, surfactant and initiator, and the temperature of the reactor were used as manipulating variables to produce terpolymers of desired composition, molecular weight distribution (MWD) and particle size distribution (PSD). The particle size polydispersity index (PSPI), molecular weight polydispersity index (MWPI) and the overall terpolymer composition ratios were incorporated in the objective functions to optimise the PSD, MWD and terpolymer composition, respectively. The optimised operational policies were validated with experiments via one stirred tank polymerisation reactor.     

Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

Treatment of metalworking fluids: development of a bioconsortium for the treatment of nanofiltration permeate

Traditional treatment methods of waste metalworking fluids produce an aqueous phase containing toxic components and with a high Chemical Oxygen Demand (COD). In this paper, a biological solution is proposed to further reduce both the effluent COD and its toxicity. A method to develop a bioconsortium from microbes found in the waste metalworking fluid is proposed. The development took place in three phases: feasibility, bioconsortium development and optimisation. Flask tests have been used to show the feasibility of using the metalworking fluid indigenous microbial community for the degradation of the nanofiltration permeate of the metalworking fluid. A suspended bioreactor allowed the development of a better‐adapted consortium. Finally, a fixed bed bioreactor inoculated with the developed bioconsortium was set up and run for 8 months to test the bioconsortium's robustness and to optimise the biological process. A bioconsortium was successfully developed using a simple method and a 90% reduction in the original nanofiltration COD level was achieved by the fixed bed bioreactor. Copyright © 2005 Society of Chemical Industry     

Response surface methodology with prediction uncertainty: A multi-objective optimisation approach

In the field of response surface methodology (RSM), the prediction uncertainty of the empirical model needs to be considered for effective process optimisation. Current methods combine the prediction mean and uncertainty through certain weighting strategies, either explicitly or implicitly, to form a single objective function for optimisation. This paper proposes to address this problem under the multi-objective optimisation framework. Overall, the method iterates through initial experimental design, empirical modelling and model-based optimisation to allocate promising experiments for the next iteration. Specifically, the Gaussian process regression is adopted as the empirical model due to its demonstrated prediction accuracy and reliable quantification of prediction uncertainty in the literature. The non-dominated sorting genetic algorithm II (NSGA-II) is used to search for Pareto points that are further clustered to give experimental points to be conducted in the next iteration. The application study, on the optimisation of a catalytic epoxidation process, demonstrates that the proposed method is a powerful tool to aid the development of chemical and potentially other processes.     

Partial least squares,steepest descent,and conjugate gradient for regularized predictive modeling

In this article, we explore the connection of partial least squares (PLS) to other regularized regression algorithms including the Lasso and ridge regression, and consider a steepest descent alternative to the PLS algorithm. First, the PLS latent variable analysis is emphasized and formulated as a standalone procedure. The PLS connections to the conjugate gradient, Krylov space, and the Cayley–Hamilton theorem for matrix pseudo-inverse are explored based on known results in the literature. Comparison of PLS with the Lasso and ridge regression are given in terms of the different resolutions along the regularization paths, leading to an explanation of why PLS sometimes does not outperform the Lasso and ridge regression. As an attempt to increase resolutions along the regularization paths, a steepest descent PLS is formulated as a regularized regression alternative to PLS and is compared to other regularized algorithms via simulations and an industrial case study.     

Hint: An educational software for heat exchanger network design with the pinch method

A free piece of educational software for heat exchanger network design based on the pinch method is presented. The interface of the program has been designed focusing on getting a clear presentation of the concepts of the pinch design methods and letting students the control of all the stages of the design. The program guides students through all the main steps of the design, including energy and cost targeting, utilities selection, heat exchanger network specification, and optimisation and retrofitting analysis. The program has been tested in a MSc pinch technology course for several years, and the feedback obtained from students during this period has been used to optimise the program functionality and its interface. The use of the program improves the efficiency of the course since it let students practice the concepts of the pinch method while relieving them of tedious repetitive calculations.     

White's method of thermodynamic calculations applied to ammoxidation of propylene

A method of steepest descent first applied by White et al. to the calculation of complex chemical equilibrium was used in this article to compute the equilibrium composition of a mixture resulting in the ammoxidation of propylene. As many as twenty species are likely to be present, some in insignificant quantities. A FORTRAN II Program was written for an IBM 1620 computer and calculations were made varying the feed composition, temperature and pressure. The results were discussed and qualitatively compared with those of the plant practice reported in the literature. The agreement is reasonably good when the oxygenated compounds are excluded from consideration.     

ON-LINE OPTIMIZATION OF DISTILLATION COLUMNS IN SERIES

This paper presents an on-line optimization algorithm for distillation columns which consists of a steepest descent technique based on a simple model of product recovery with on-line estimation of the critical model parameter. The algorithm is developed for a single column and then for a two-column train. A dynamic programming approach is used to reduce the optimization problem to a single parameter search for each column in the train. The resulting algorithm is simple and computer resource requirements are small. The algorithm has been successfully used in two industrial applications, one consisting of two columns in series and the other of three columns in series.     

Advanced enterprise resource management systems for the batch industry. The TicTacToe algorithm

Since 1997 the authors have prototyped an enterprise resource management (ERM) system recommending essential improvements to the available software packages, which today claim automatic real time performance and an efficient transaction-oriented approach. The ERM system proposed is a decision-making tool for the manufacturing industry, which makes integrated financial/production trade-off planning and optimisation in the supply chain management. The application supports management cycles because during its use are available degrees of freedom to make the system work as a decision-making tool. The enterprises must be aware of its internal problems to be dynamically approachable instead of prematurely evolving to networked enterprises by links in the electronic commerce. ERM systems are capable of managing dynamically realistic and optimal delivery dates with price-time trade-off during the marketing activities. Cost objective functions integrated with system performance measures are developed, including a cost model to evaluate the due date policy economics in real time. The order management system proposed is based on the TicTacToe algorithm, which follows an exact non-combinatorial deterministic approach to time and optimise production sequences in multiproduct plants in very short time using asymmetric travelling salesman problem formulations for the scheduling task. The resulting tool is appropriate to fulfil the requirements of autonomous order entry systems in integrated real time systems.     

锅炉循环清洗调度问题优化

多操作周期的锅炉蒸汽系统是一个性能随时间衰减的混合整数非线性规划问题,其目标函数由燃煤量和锅炉清洗维护的费用构成。针对此类非线性问题,提出了最速下降近似线性规划（SDALP）的算法,利用最快速下降的目的性搜索思想,引入移入、移出向量和积极富裕量,通过对调整变量边界的判断条件和途径进行了新的定义,排除了传统近似线性规划方法中主观选择初始可行解、步长限制量以及缩小系数等变量可能导致的将最优解排除在变量边界之外的问题。将该算法应用于锅炉蒸汽系统循环清洗调度问题,实验结果表明,SDALP算法解出的调度方案优于原电厂调度方案,节能效果明显。     

温度诱导双水相分离纯化细菌素工艺条件的选取与优化

利用TritonX-114可萃取疏水性蛋白质的性质,研究了温度诱导双水相分离纯化细菌素乳链菌肽, 用响应面法对成相成份TritonX-114和离子强度进行了优化,最佳浓度分别为3.27% TritonX-114,0.85 mol/L氯化钠. 优化过程中,pH固定为2.0,温度固定为30℃, 在此条件下乳链菌肽的萃取率为92%,纯化系数为9.05.