Design Of Batch Processes Research Articles

Modelling solution speciation to predict pH and supersaturation for design of batch and continuous organic salt crystallisation processes

Organic salt crystallisation is of great importance to the pharmaceutical industry as the majority of pharmaceutical products are marketed as salts. In this study a solution speciation model was developed and experimentally validated to predict pH and supersaturation during organic salt crystallisation processes when only the overall solution composition is known. By solving a particular system of equations simultaneously the full speciation and pH of the solution is calculated. Simply with knowledge of only the overall solution composition, dissociation constants and solubility products this model can be used to generate the design space for organic salt crystallisation and gain deeper understanding of the salt crystallisation process. In particular, the model can be used to calculate the maximum theoretical crystallisation yield and the corresponding mother liquor pH which would be impossible to determine otherwise without performing experiments. The case study used in this work is the polymorphic organic salt ethylenediammonium 3,5-dinitrobenzoate (EDNB) which is the 2:1 salt of 3,5-dinitrobenzoic acid (3,5-DNBA) with ethylenediamine. In this system three solid forms can be produced: EDNB monoclinic, EDNB triclinic and/or the 3,5-dinitrobenzoic acid (3,5-DNBA) starting material. The solution speciation model was used to investigate the crystallisation process for each solid form and test different feed molar ratios and different solution addition/mixing approaches for semi-batch and continuous processes.

Modeling and simulation-based design of electroenzymatic batch processes catalyzed by unspecific peroxygenase from A. aegerita.

Unspecific peroxygenases have attracted interest due to their ability to catalyze the oxygenation of various types of C-H bonds using only hydrogen peroxide as a cosubstrate. Due to the instability of these enzymes at even low hydrogen peroxide concentrations, careful fed-batch addition of the cosubstrate or ideally in situ production is required. While various approaches for hydrogen peroxide addition have been qualitatively assessed, only limited kinetic data concerning enzyme inactivation and peroxide accumulation has been reported so far. To obtain quantitative insights into the kinetics of such a process, a detailed data set for a peroxygenase-catalyzed benzylic hydroxylation coupled with electrochemical hydrogen peroxide production is presented. Based on this data set, we set out to model such an electroenzymatic process. For this, initial velocity data for the benzylic hydroxylation is collected and an extended Ping-Pong-Bi-Bi type rate equation is established, which sufficiently describes the enzyme kinetic. Moreover, we propose an empirical inactivation term based on the collected data set. Finally, we show that the full model does not only describe the process with sufficient accuracy, but can also be used predictively to control hydrogen peroxide feeding rates To limit the concentration of this critical cosubstrate in the system.

Integrated process design, scheduling, and model predictive control of batch processes with closed‐loop implementation

AbstractSimultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed‐integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed‐loop implementation. We use multi‐parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base‐2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.

The design and scheduling of chemical batch processes: Computational complexity studies

Summary The pharmaceutical industry is quite restrictive concerning quality and safety, the manufacturing disruptions often lead to drug shortages in despite of the high costs involved. Due to the minimization of equipment costs, the design and scheduling of chemical batch processes (DSCBP) is a well-known problem, and various sub-problems are selected from the literature because they were successively enlarging the design and schedule policies: single machine or multiple machines (S or M) in each stage; and single product campaigns or multiple products campaigns (SPC or MPC). In this paper, four problems are studied (by combinatorics: SS, MS, SM, and MM) and it is shown that they are all NP-hard in strong sense through polynomial reduction. This study can support innovative algorithms and methodologies for solving DSCBP problems, in a way to improve equipments sizing and configurations design, and thereby contributing to curb disruptions within pharmaceutical supply chains (PharmSC).

Dynamic optimization of fixed bed chemical-looping combustion systems integrated in thermal power plants

Optimization methods can be used to improve the design and operation of batch processes. In this work, we illustrate the application of dynamic simulation and optimization for power generation in chemical-looping combustion systems, carried out in batch reactors. We present a framework for the optimization strategy, in which the objective is to maximize a measure of the energy efficiency, bounded by inequality constraints reflecting the industry standards. Several case studies are used to illustrate the applicability of the present problem formulation to enhance the process performance.

Flexible Batch Process and Plant Design Using Mixed-Logic Dynamic Optimization: Single-Product Plants

Batch manufacturing plants require the frequent adaptation of production facilities to incorporate internal and external uncertainties. The introduction of flexibility in the design of general-purpose plants represents a cornerstone for the design and retrofit of batch processes along the plant’s lifecycle. Moreover, the integration of batch process development decisions during the plant design also contributes to handle uncertainty. This paper presents a novel optimization-based approach for the flexible design of batch plants by integrating process synthesis and plant allocation decisions, such as feed-forward control trajectories of processing conditions or the selection of operating modes and equipment items. To do so, a modeling strategy based on Mixed-Logic Dynamic Optimization and two-stage stochastic formulation is used to maximize the expected profit taking into account several uncertain scenarios. Moreover, a heuristic algorithm is proposed to solve the resulting problem that is characterized by a great complexity, consisting of an iterative procedure with embedded deterministic solutions of the integrated problem. The proposed approach is illustrated through the design of a single-product batch plant to carry out a competitive reaction process.

A Mixed-Integer Memetic Algorithm Applied to Batch Process Optimization

Evolutionary algorithms (EAs) are population-based global search methods. Memetic Algorithms (MAs) are hybrid EAs that combine genetic operators with local search methods. With global exploration and local exploitation in search space, MAs are capable of obtaining more high-quality solutions. On the other hand, mixed-integer hybrid differential evolution (MIHDE), as an EA-based search algorithm, has been successfully applied to many mixed-integer optimization problems. In this paper, a mixed-integer memetic algorithm based on MIHDE is developed for solving mixed-integer constrained optimization problems. The proposed algorithm is implemented and applied to the optimal design of batch processes. Experimental results show that the proposed algorithm can find a better optimal solution compared with some other search algorithms.

Toward an inherently safer design and operation of batch and semi-batch processes: The N-oxidation of alkylpyridines

This work shows an application of inherent safety principles to a reaction widely used in the pharmaceutical industry. More specifically, it incorporates the teachings of Trevor Kletz into the design of an inherently safer process for the N-oxidation of alkylpyridines. This reaction is of interest because of the hazards resulting from the undesired, gas-generating decomposition of hydrogen peroxide, the oxidizing agent. The generation of oxygen, combined with the flammability of the alkylpyridines, represents a serious fire and explosion hazard for this process. The purpose of this paper is to demonstrate how an inherently safer process can be potentially achieved by designing improved reactors and by assessing conditions that reduce or eliminate the hazards. Furthermore, it is shown that such improvement in safety increases the efficiency of the process and results in a cost reduction.

Optimizing the Initial Conditions To Improve the Dynamic Flexibility of Batch Processes

It is shown in this paper that by changing the initial operation condition of batch processes, the dynamic performance of the system can be varied largely. The initial operation conditions are often ignored in design of batch processes for flexibility against disturbances or parameter variations. When the initial conditions are not rigid, as in the case of a batch reactor where the initial reaction temperature is quite arbitrary, optimization can also be applied to determine the “best” initial condition to be used. Problems for dynamic flexibility analysis, including initial conditions and process operation, can be formulated as dynamic optimization problems. If the initial conditions are considered, the conditions can be transferred into control variables in the first step of optimization. The solution of the dynamic optimization is based on the Runge-Kutta integration algorithm and decomposition search algorithm. This method, as illustrated and tested with two highly nonlinear chemical engineering problems, enables the optimal solution to be determined.

Optimization of multi-product batch plant design under uncertainty with environmental considerations

This paper presents a framework for the design and optimization of multi-product batch processes under uncertainty with environmental considerations. The uncertainties and environmental impacts are discussed. The profit and environmental impacts are considered as bi-objectives for batch plant design. The problem, thus, is formulated as a multi-objective stochastic programming problem. It can be converted into a single-objective two-stage stochastic linear programming problem using the weighted aggregation method. To solve the two-stage stochastic programming, we introduce both Monte Carlo sampling for the entire domain of the distribution function and the feasibility cut method based on dual theory in Benders’ decomposition. The detailed algorithm for problem-solving is presented. A numerical example is presented to illustrate the proposed framework.

Simultaneous optimal synthesis, design and operation of batch and continuous hybrid separation processes

A procedure for the optimal determination of process synthesis, design and operation for both batch and continuous separation is presented. The procedure allows the simultaneous determination of the optimal process from any specified available process alternatives, its configuration, its design and corresponding operation in order to perform a given separation duty. In this work, distillation, pervaporation and hybrid distillation/pervaporation processes are considered as process alternatives. The optimisation strategy takes into account an overall economic index that encompasses capital investment, operating costs and production revenues. Furthermore, rigorous mathematical models developed from first principles for distillation and pervaporation are used. Case studies are presented and it is found that hybrid distillation/pervaporation processes are the optimal synthesis solution in most cases and large costs savings can be made compared to traditional operation based on distillation alone.

Integration between Scheduling and Design of Batch Systems Based on Petri Net Models

A batch process is a discontinuous and concurrent process which is suitable for multi-product, small-sized production. The distinctive feature of a batch process is that various decision making processes, such as scheduling, design, operation, etc. are strongly connected with each other. Interaction among these processes is necessary to dynamically and flexibly cope with a variety of unplanned events. This paper aims at presenting a batch scheduling technique based on Petri net models and showing the possibilities of integration between scheduling and design of batch processes. For this purpose, it first views the behavior of a batch operating system as a discrete event system and presents a Petri net model to be used for scheduling, design and operation. It next formulates batch scheduling problems based on Petri net partial languages, proposes their solution technique and last discusses the integration between scheduling and design of batch systems.

Design of Controllable Batch Processes in the Presence of Uncertainty

Design of controllable batch processes can be more challenging than continuous processes because of their unsteady nature of operation. The operating strategy of a batch process is characterized by trajectories of manipulated variables. This precludes the use of conventional controllability measures in evaluating the controllability of a given batch process design. Short process development cycles typically accredited to batch processes lead to uncertainty in the model formulation. Integrated approach to batch process design and control addresses the problem of controllability of a batch process during the design phase. This is best achieved by treating the problem as a dynamic optimization problem with time invariant (design) and time variant (operating) variables.

Computational studies using a novel simplicial-approximation based algorithm for MINLP optimization

Significant advances have been made in the last two decades for the effective solution of mixed integer non-linear programming (MINLP) problems, mainly by exploiting the special structure of the problem that results under certain convexity assumptions. This paper discusses the computational experience with a novel decomposition algorithm which is based on the idea of closely approximating the feasible region defined by the set of constraints by a convex polytope using the simplicial approximation approach (Goyal and Ierapetritou, 2003b). A variety of problems are solved including structural flowsheet optimization, design of batch processes and trim-loss minimization to illustrate the applicability and efficiency of the proposed approach.

Computational studies using a novel simplicial-approximation based algorithm for MINLP optimization

Significant advances have been made in the last two decades for the effective solution of mixed integer non-linear programming (MINLP) problems, mainly by exploiting the special structure of the problem that results under certain convexity assumptions. This paper discusses the computational experience with a novel decomposition algorithm which is based on the idea of closely approximating the feasible region defined by the set of constraints by a convex polytope using the simplicial approximation approach (Goyal and Ierapetritou, 2003b). A variety of problems are solved including structural flowsheet optimization, design of batch processes and trim-loss minimization to illustrate the applicability and efficiency of the proposed approach.

A New Algorithm for Cyclic Scheduling and Design of Multipurpose Batch Plants

Many researchers have studied the scheduling, planning, and design of multipurpose batch processes. However, not so many studies have treated design and scheduling or design and planning simultaneously. The complexity of these systems stems from the fact that plant configuration must be determined for the purpose of process scheduling, yet scheduling must be done to devise the plant configuration. In the present study, a new algorithm for determining the best multipurpose scheduling and plant configuration is suggested. Since the objective function of the problem is nonlinear, it is linearized using a separable programming method. The proposed method consists of a number of procedures. First, a feasible configuration is obtained. Next, both the optimum equipment size and cyclic scheduling are determined for the plant configuration obtained in the first procedure. Last, the evolutionary design method proposed by Fuchino et al.(J. Chem. Eng. Jpn. 1994, 27, 57−64) is used to find the solution that minimizes the total cost. The efficacy of the proposed approach is demonstrated in three examples. Usually, equipment sizes are considered by a continuous variable in mixed integer linear programming and task processing time is assumed to be constant. However, most types of equipment are manufactured only in discrete volume classes. In addition, processing times are dependent on batch sizes. Hence, to apply the optimization methods developed here to real industries, the method was modified such that the volumes of equipment are considered as discrete variables and the processing time is a function of batch size.

An approach to optimal design of batch processes with waste minimization

A formulation of multi-objective optimization problems is proposed for the optimal design of batch processes with waste minimization. An EQW (effective quantity of waste) is defined as a standard of environmental impact assessment. Four cases for an example problem are studied with different weight policies and the results are compared and analyzed.

Batch process design and operation using operational envelopes

We consider the problem of determining the details of the design and operation of batch processes as relevant to the pharmaceuticals and fine and specialty chemicals sectors. The aim is to improve the early phase process development activity to result in more efficient processes and plants in a much shorter period of time, thus reducing both the time to market and capital and operating costs. The particular component described here is the use of “operational envelopes” in the process design activity. This recognises that existing processes never operate as specified through the design intent, but rather in some region around the design specifications. Instead of designing processes to operate at a fixed point and then implicitly allow variation, we aim to design processes to operate within an explicit operational envelope. Bounds on desired dimensions of the envelope are specified and an optimisation procedure is utilised to generate processes with desired characteristics.

Design of integrated batch processes with discrete and continuous equipment sizes

This paper presents a mathematical programming approach for the integrated design of batch processes involving multiple processing steps. Detailed dynamic models are used to describe the behaviour of individual batch operations. The mathematical formulation regards both the design ( e.g. equipment items with discrete sizes) and operational characteristics ( i.e. control variable profiles such as reflux ratio, cooling water flowrate) of each task as degrees-of-freedom, the values of which are selected to optimise a particular objective function ( e.g. profit). The solution may be restricted by inequality constraints which hold throughout the task ( e.g. reactor temperature violation) or at the end of the task ( e.g. end-product purity specification). The sizes of the equipment items are treated as discrete decisions and are chosen from a selection of standard sizes. The mathematical formulation leads to a mixed integer dynamic optimisation problem and is solved using the outer approximation/augmented penalty (OA/AP) algorithm.

Environmental impact considerations in the optimal design and scheduling of batch processes

A systematic methodology for incorporating ecological considerations in the optimal design and scheduling of batch/semi-continuous processes is presented in this paper. The methodology embeds principles from Life Cycle Analysis (LCA) within a general multi-objective formulation for the design of multipurpose batch plants, with process economics and environmental impact as distinct design objectives. An expanded boundary is defined around the process of interest, for the consistent evaluation of environmental impact, which is quantified by a set of metrics (for air, water pollution, global warming etc.). Examples from the dairy industry are presented to demonstrate the potential of the methodology to assist in arriving at environmentally friendly and economically favourable batch designs and schedules. Issues regarding the use of alternative cleaning and legislation policies on batch operation and design are also discussed.