MODELING AND OPTIMIZATION OF LACTIC ACID SYNTHESIS BY THE ALKALINE DEGRADATION OF FRUCTOSE IN A BATCH REACTOR

The present work deals with the determination of the optimal operating conditions of lactic acid synthesis by the alkaline degradation of fructose. It is a complex transformation for which detailed knowledge is not available. It is carried out in a batch or semi-batch reactor. The “Tendency Modeling” approach, which consists of the development of an approximate stoichiometric and kinetic model, has been used. An experimental planning method has been utilized as the database for model development. The application of the experimental planning methodology allows comparison between the experimental and model response. The model is then used in an optimization procedure to compute the optimal process. The optimal control problem is converted into a nonlinear programming problem solved using the sequencial quadratic programming procedure coupled with the golden search method. The strategy developed allows simultaneously optimizing the different variables, which may be constrained. The validity of the methodology is illustrated by the determination of the optimal operating conditions of lactic acid production.     

Modeling and global optimization of DNA separation

We develop a non-convex non-linear programming problem that determines the minimum run time to resolve different lengths of DNA using a gel-free micelle end-labeled free solution electrophoresis separation method. Our optimization framework allows for an efficient determination of the utility of different DNA separation platforms and enables the identification of the optimal operating conditions for these DNA separation devices. The non-linear programming problem requires a model for signal spacing and signal width, which is known for many DNA separation methods. As a case study, we show how our approach is used to determine the optimal run conditions for micelle end-labeled free-solution electrophoresis and examine the trade-offs between a single capillary system and a parallel capillary system. Parallel capillaries are shown to only be beneficial for DNA lengths above 230 bases using a polydisperse micelle end-label otherwise single capillaries produce faster separations.     

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer^® with an external NLP solver implemented in Matlab^®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.     

Design of reverse osmosis networks for multiple freshwater production

Reverse osmosis (RO) desalination, which produces multiple freshwater from seawater, has been studied in this work. An optimization method based on process synthesis has been applied to design the RO system. First, a simplified superstructure that contains all the feasible design for this desalination problem has been presented. In this structural representation, the stream split ratios and the logical expressions of stream mixing were employed, which can make the mathematical model easy to handle. Then, the membrane separation units employing the spiral wound reverse osmosis elements were described by using a pressure vessel model, which takes into account the pressure drop and the concentration changes in the membrane channel. The optimum design problem can be formulated as a mixedinteger non-linear programming (MINLP) problem, which minimizes the total annualized cost of the RO system. The cost equation relating the capital and operating cost to the design variables, as well as the structural variables, has been introduced in the objective function. The problem solution includes the optimal streams distribution, the optimal system structure and the operating conditions. The design method could also be used for the optimal selection of membrane element type in each stage and the optimal number of membrane elements in each pressure vessel. The effectiveness of this design methodology has been demonstrated by solving a desalination case. The comparisons with common industrial approach indicated that the integrative RO system proposed in this work is more economical, which can lead to significant capital cost and energy saving and provide an economically attractive desalination scheme.     

Targeting optimum resource allocation using reverse problem formulations and property clustering techniques

Decoupling the constitutive equations from the balance and constraint equations allows for reformulating a conventional forward problem into two reverse problems. The first reverse problem is the reverse of a simulation problem, where the process model is solved in terms of the constitutive (synthesis/design) variables instead of the process variables, thus providing the synthesis/design targets. The second reverse problem (reverse property prediction) solves the constitutive equations to identify unit operations, operating conditions and/or products by matching the synthesis/design targets. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear “mixing” rules, which allows for the development of consistent additive rules along with their ternary representation.     

Optimisation-based design method for membrane-assisted separation processes

This paper presents a design method for membrane-assisted separation processes based on the concept of process superstructure optimisation, which should be applied to the separation of azeotropic mixtures. The main features of the proposed method are as follows: (i) detailed rate-based modelling of all unit operations; (ii) experimental model identification for membrane separation; (iii) application of an evolutionary algorithm. This method allows the simultaneous determination of optimal process configuration, equipment design and operating conditions for membrane-assisted separation processes.A case study for the separation of a ternary mixture of acetone, isopropyl alcohol and water in a hybrid pervaporation-distillation process is presented using the optimisation-based design method. Detailed rate-based models for the unit operations involved were implemented in a generic process model and necessary membrane model parameters were determined experimentally in a laboratory-scale device for the hydrophilic polymeric membrane Pervap™ 2201D from Sulzer Chemtech. After the identification of an appropriate process superstructure, the process configuration, dimensions of equipment and operating conditions required for the optimal hybrid pervaporation-distillation process were determined simultaneously. The optimisation criterion was the cost for purifying one ton of acetone. The results show that the developed method can be applied successfully for this complex mixed-integer non-linear optimisation problem.     

Membrane system design for multicomponent gas mixtures via mixed-integer nonlinear programming

An optimal design strategy for membrane networks separating multicomponent gas mixtures based on an approximate permeator model and mixed-integer nonlinear programming (MINLP) is proposed. A permeator system superstructure is used to embed a very large number of possible network configurations and allows the permeator feed-side pressure to be fixed or a design variable. A MINLP design model is developed to minimize the total annual process cost by simultaneous optimization of the permeator configuration and operating conditions. Case studies for the separation of acid gases (CO₂ and H₂S) from crude natural gas mixtures with spiral-wound permeators are presented. Permeator configurations are derived for different number of separation stages for both continuous and discrete membrane areas. The method is sufficiently robust to handle product compositions that vary five orders of magnitude. The proposed approach provides an efficient methodology for preliminary screening of multi-stage membrane separation systems for multicomponent gas mixtures.     

Precise parameter estimation for chemical batch reactions in heterogeneous medium

A sequential experimental strategy for precise parameter estimation has been used in the case of liquid-liquid dispersions in batch-stirred tank reactors where slow chemical reactions take place. The mathematical model for a batch reaction in a stirred tank reactor is formulated as a system of non-linear differential equations standing for the mass balance of each component. Physical kinetic parameters and chemical kinetic parameters which arise from this model are estimated simultaneously. The estimation problem is posed as a weighted least squares problem and solved by using a standard Levenberg-Marquardt algorithm. In this work, we intend to show how it is possible to develop efficient experimental design strategies that lead to an accurate estimation of the parameters involved in phenomenological models and most particularly in kinetic models. Three design criteria for designing the experiments have been employed in order to increase the precision on the parameter estimates of the model. A standard non-linear sequential quadratic programming method ensures the determination of the operating conditions which define the experimental design. The well-known alkaline hydrolysis of esters in aqueous phase has been treated as a numerical application example.     

一种新的生产计划与催化裂化装置过程操作集成的闭环策略

Production planning models generated by common modeling systems do not involve constraints for process operations, and a solution optimized by these models is called a quasi-optimal plan. The quasi-optimal plan cannot be executed in practice some time for no corresponding operating conditions. In order to determine a practi- cally feasible optimal plan and corresponding operating conditions of fluidized catalytic cracking unit （FCCU）, a novel close-loop integrated strategy, including determination of a quasi-optimal plan, search of operating conditions of FCCU and revision of the production planning model, was proposed in this article. In the strategy, a generalized genetic algorithm （GA） coupled with a sequential process simulator of FCCU was applied to search operating conditions implementing the quasi-optimal plan of FCCU and output the optimal individual in the GA search as a final genetic individual. When no corresponding operating conditions were found, the final genetic individual based correction （FGIC） method was presented to revise the production planning model, and then a new quasi-optimal production plan was determined. The above steps were repeated until a practically feasible optimal plan and corresponding operating conditions of FCCU were obtained. The close-loop integrated strategy was validated by two cases, and it was indicated that the strategy was efficient in determining a practically executed optimal plan and corresponding operating conditions of FCCU.     

换热网络运行模拟优化

换热网络系统大多数是按给定工况,以投资费用和运行费用最优为目标设计的。但在实际运行过程中,确定和不确定性的影响因素往往导致换热网络的运行工况偏离设计值。偏离设计工况运行的换热网络性能变差,导致运行费用增加,甚至不能满足工艺物流换热要求。在换热网络结构给定条件下,将满足物流目标温度和运行费用最优作为目标函数,以单体模型和Yee et al.（1990）提出的多级超结构为基础,建立换热网络运行模拟优化模型,并进一步去除恒定膜传热系数假设使模型贴近实际问题。针对提出的非线性数学模型（NLP）问题,以标准粒子群算法为基础建立求解策略。在论文的最后,4个来源于已发表论文的实例研究证明了该优化方法的有效性。     

Modelling and constraint optimisation of an aromatic nitration in liquid–liquid medium

A methodology, which determines the operating conditions simultaneously optimising the chemical yield and considering the safety aspect, has been developed for a chemical reaction which is carried out batch-wise. To illustrate the methodology, the aromatic nitration of toluene by mixed acid has been chosen as a typical exothermic and non-selective reaction. This reaction takes place in a two-phase medium and, therefore, involves simultaneously chemical reaction and mass transfer phenomena. A kinetic model recently proposed for the slow and fast liquid–liquid reaction regimes was integrated to the mass balance. Nitration experiments were carried out in order to compare experimental composition profiles with simulated ones. Afterwards, an optimisation procedure has been used to maximise conversion, by manipulating the operating conditions subject to safety constraints. The p-nitrotoluene yield was chosen as the criterion to be maximised. Experimental validation for the optimisation procedure has been carried out. A monofluid heating–cooling system controlled by a predictive controller was used for the temperature control of the reactor. Simulation and experimental results are presented, discussed and compared.     

Classical and alternative fuel mix optimization in cement production using mathematical programming

In this paper a systematic methodology is presented for the simultaneous optimal selection of raw materials, fossil fuels and alternative fuels in cement production. The aim is to offer a generic mathematical formulation that can be used as the basis for developing case specific mathematical formulations that can assist the strategic decision-making process. The mathematical model presented takes into consideration the essential elements of a cement plant operation. The final formulation is a mixed integer linear programming problem that aims at minimizing the overall operating cost. A realistic case study is presented which demonstrates the usefulness of the proposed mathematical programming methodology.     

SMB chromatography design using profile advancement factors,miniplant data,and rate‐based process simulation

This article describes a systematic miniplant‐based approach to rapid development of simulated moving bed (SMB) chromatography applications. The methodology involves analysis of single‐column pulse tests to screen adsorbents and operating conditions and to determine initial values of profile advancement factors used to specify flow rates for an initial SMB miniplant experiment. A lumped‐parameter linear driving force rate‐based model is developed by fitting process data from a single miniplant run. The data are fit in a two‐step procedure involving initial determination of effective adsorption isotherm constants as best‐fit parameters with subsequent adjustment of calculated mass transfer coefficients to refine the data fit. The resulting simulation is used to guide further miniplant work and minimize experimental effort. The methodology is illustrated with miniplant data for a binary protein separation showing excellent agreement between model results and process data generated over a wide range of operating conditions. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Optimal operation of simulated-moving-bed reactors for nonlinear adsorption isotherms and equilibrium reactions

An explicit design procedure for simulated-moving-bed reactors (SMBRs) is presented, incorporating a nonlinear, competitive isotherm, and an equilibrium reaction involving three species, e.g., A⇔B+C. This design procedure is based on an equilibrium theory model of a true-moving-bed (TMB) reactor and it is in close analogy to the well-known design procedure for purely separative SMB-units, the so-called ‘triangle theory’ (Journal of Chromatography A, 769, 3 (1997)). It allows an easy determination of the optimal point of operation and can also evaluate the robustness of an operation point. The operating region of full conversion and complete separation for the TMBR is compared to the one of a purely separative TMB unit, to which only the products are fed. The underlying explicit relations for the new design procedure are presented and their reliability is validated by comparison with experimental data of a SMBR for the synthesis of methyl acetate.     

Rolling horizon based planning and scheduling integration with production capacity consideration

The rolling horizon method has been proposed to address the integrated production planning and scheduling optimization problem. Since the method can generally result in small-scale optimization model and fast solution, it has been used in a number of applications in realistic industrial planning and scheduling problems. In this paper, it is first pointed out that the incorporation of valid production capacity information into the planning model can improve the solution quality in the rolling horizon solution framework. A novel method is then proposed to derive the production capacity information representing the detail scheduling model based on parametric programming technique. A heuristic process network decomposition strategy is further applied to reduce the computational effort needed for larger and more complex process networks. Several case studies have been studied, which illustrate the efficiency of the proposed methodology in improving the solution quality of rolling horizon method for integrated planning and scheduling optimization.     

Lactic acid production from dining‐hall food waste by Lactobacillus plantarum using response surface methodology

BACKGROUND: Food waste generally has a high starch content and is rich in nutritional compounds, including lipids and proteins. It therefore represents a potential renewable resource. In this study, dining‐hall food waste was used as a substrate for lactic acid production, and response surface methodology was employed to optimise the fermentation conditions. RESULTS: Lactic acid biosynthesis was significantly affected by the interaction of protease and temperature. Protease, temperature and CaCO₃ had significant linear effects on lactic acid production, while α‐amylase and yeast extract had insignificant effects. The optimal conditions were found to be an α‐amylase activity of 13.86 U g^?1 dried food waste, a protease activity of 2.12 U g^?1 dried food waste, a temperature of 29.31 °C and a CaCO₃ concentration of 62.67 g L^?1, which resulted in a maximum lactic acid concentration of 98.51 g L^?1 (88.75% yield). An increase in inoculum size would be appropriate for accelerating the depletion of initial soluble carbohydrate to enhance the efficiency of α‐amylase in dining‐hall food waste fermentation. CONCLUSION: A suitable regression model for lactic acid production was developed based on the experimental results. Dining‐hall food waste was found to be a good substrate for lactic acid fermentation with high product yield and without nutrient supplementation. Copyright © 2008 Society of Chemical Industry     

A hybrid genetic algorithm for efficient parameter estimation of large kinetic models

The development of predictive models is a time consuming, knowledge intensive, iterative process where an approximate model is proposed to explain experimental data, the model parameters that best fit the data are determined and the model is subsequently refined to improve its predictive capabilities. Ascertaining the validity of the proposed model is based upon how thoroughly the parameter search has been conducted in the allowable range. The determination of the optimal model parameters is complicated by the complexity/non-linearity of the model, potentially large number of equations and parameters, poor quality of the data, and lack of tight bounds for the parameter ranges. In this paper, we will critically evaluate a hybrid search procedure that employs a genetic algorithm for identifying promising regions of the solution space followed by the use of an optimizer to search locally in the identified regions. It has been found that this procedure is capable of identifying solutions that are essentially equivalent to the global optimum reported by a state-of-the-art global optimizer but much faster. A 13 parameter model that results in 60 differential-algebraic equations for propane aromatization on a zeolite catalyst is proposed as a more challenging test case to validate this algorithm. This hybrid technique has been able to locate multiple solutions that are nearly as good with respect to the “sum of squares” error criterion, but imply significantly different physical situations.     

Capacity expansion planning through augmented Lagrangian optimization and scenario decomposition

Stochastic programming is a typical method for addressing the uncertainties in capacity expansion planning problem. However, the corresponding deterministic equivalent model is often intractable with considerable number of uncertainty scenarios especially for stochastic integer programming (SIP) based formulations. In this article, a hybrid solution framework consisting of augmented Lagrangian optimization and scenario decomposition algorithm is proposed to solve the SIP problem. The method divides the solution procedure into two phases, where traditional linearization based decomposition strategy and global optimization technique are applied to solve the relaxation problem successively. Using the proposed solution framework, a feasible solution of the original problem can be obtained after the first solution phase whereas the optimal solution is obtained after the second solution phase. The effectiveness of the proposed strategy is verified through a numerical example of two stage stochastic integer program and the capacity expansion planning examples. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

DETERMINATION OF MASS TRANSPORT PARAMETERS FOR FIXED-BED ADSORBERS

An innovative technique has been developed for determination of external (“film”) and internal (“intraparticle”) mass transport parameters associated with uptake of solutes by porous solids in fixed-bed adsorbers. These parameters are required for predictive modeling of the dynamics of such systems as activated carbon columns and ion exchange beds. Current methods for evaluation of film transfer coefficients and effective intraparticle diffusivities involve correlation procedures and separate batch-reactor measurements of adsorption rates, respectively. These methods frequently introduce and compound significant errors in subsequent predictive modeling Tor full-scale process design. The present work develops a method by which, on the basis of the characteristics of breakthrough curves measured in specially designed micro-columns, more accurate and reliable simultaneous determinations of both film transfer coefficients and effective surface diffusivities can be done. Verification of the procedure is demonstrated by the agreement of predictions generated from a mathematical model, in which mass transfer coefficients determined from the micro-column technique are used, with experimental breakthrough data for larger scale adsorbers.