Том 51, № 6 (2017)

Articles prepared based on the materials of the American-Russian Chemical Engineering Scientific School “Modeling and Optimization of Chemical Engineering Processes and Systems”, which was held at Kazan National Research Technological University on May 23−25, 2016, have been reviewed. The development and application of modern simulation and optimization methods for solving the problems of designing, managing, and studying chemical engineering processes and systems for using rationally energy and raw materials, ensure safety and protection of the environment, and provide sustainable development are discussed.

Process systems engineering faces increasing demands and opportunities for better process modeling and optimization strategies, particularly in the area of dynamic operations. Modern optimization strategies for dynamic optimization trace their inception to the groundbreaking work Pontryagin and his coworkers, starting 60 years ago. Since then the application of large-scale non-linear programming strategies has extended their discoveries to deal with challenging real-world process optimization problems. This study discusses the evolution of dynamic optimization strategies and how they have impacted the optimal design and operation of chemical processes. We demonstrate the effectiveness of dynamic optimization on three case studies for real-world reactive processes. In the first case, we consider the optimal design of runaway reactors, where simulation models may lead to unbounded profiles for many choices of design and operating conditions. As a result, optimization based on repeated simulations typically fails, and a simultaneous, equationbased approach must be applied. Next we consider optimal operating policies for grade transitions in polymer processes. Modeled as an optimal control problem, we demonstrate how product specifications lead to multistage formulations that greatly improve process performance and reduce waste. Third, we consider an optimization strategy for the integration of scheduling and dynamic process operation for general continuous/batch processes. The method introduces a discrete time formulation for simultaneous optimization of scheduling and operating decisions. For all of these cases we provide a summary of directions and challenges for future integration of these tasks and extensions in optimization formulations and strategies.

While standard life cycle assessment methods exclude fundamental process engineering models in its analysis, sustainable process design (SPD) is plagued by the dilemma of boundary selection that results in consideration of incomplete life cycles and shifting of emissions outside the system boundary. The Process to Planet (P2P) framework bridges this gap by combining sustainable process design with environmentally extended input output analysis. This framework extending across multiple scales provides the capability of working with process variables and designing processes at the equipment scale while considering the entire life cycle through the supply chain and economic scale models. This work expands the P2P framework to account for byproducts originating from any unit within the model. The framework is further modified to incorporate an economic objective function, henceforth developing a multiobjective (MO) optimization problem for optimal design of any generic industrial process. The modified P2P framework is demonstrated by application to a corn ethanol manufacturing process case study. The MO problem is solved using the epsilon constraint method to obtain Pareto optimal frontiers that reveal the trade-off between environmental and economic dimensions of the sustainable process design problem. Comparison between commonly practiced conventional SPD and P2P SPD Pareto curves exposes the chance of choosing non optimal solutions if the former method is employed.

In the design of chemical process under uncertainty in initial information, an important problem is to determine a structure in which the control system will guarantee that all constraints are satisfied despite variations in internal and external factors at the operation stage. A method has been proposed for solving onestage optimization problems with chance constraints in the design of optimal flexible chemical processes. The developed approach makes it possible to avoid multidimensional integration in each of the iterations of problem solving, thus reducing the computational effort. The efficiency of the proposed approach is illustrated by model examples.

System analysis of indicators of existing industries of ethylene glycols that differ in equipment and technology design has been carried out in the CHEMCAD universal software. The issues of optimizing the operating regimens of reaction and evaporation units of these plants by the creation of automated algorithm of third-party control and special macro have been considered. It has been shown that the use of this approach makes it possible to improve the technical and economical indicators of operating industries.

The research was directed at establishing the dependence of the disperse composition of particles of TiCl₄–Al(i-C₄H₉)₃ catalytically active precipitate, and their activity and molecular weight characteristics of polybutadiene formed in their presence on the length of sections and the confuser diameter of the tubular turbulent apparatus of diffuser–confuser design used to prepare a catalytic system in turbulent flows. An approach that combines methods of computational fluid dynamics (ANSYS Workbench 17.1 platform) and formal kinetics, and solves inverse problems of kinetics has been used to solve the stated problem. The following required dependences were established based on numerical experiments on the models developed using this approach: (1) the hydrodynamics of the process of dispersing particles of TiCl₄–Al(i-C₄H₉)₃ catalytically active precipitate in a tubular turbulent apparatus with diffuser–confuser design, and (2) macrokinetics of butadiene polymerization on the particles of TiCl₄–Al(i-C₄H₉)₃ catalytically active precipitate dispersed in turbulent flows (batch reactor, the solvent was toluene, and the temperature of the process was 25°С).

An experimental study has been carried out of the chemical equilibrium and chemical kinetics of the reactions of transesterification of fluoroorganic esters for two systems under static conditions. The experiments were carried out at both the boiling point of the mixture and at constant temperature. For the transesterification reaction of methyl ester of bromodifluoroacetic acid with trifluoroacetic acid, the dependence of the reaction rate on temperature has been determined.

The advantages of special smooth-wall apparatuses equipped with precession stirrers compared to typical process tanks equipped with the stiff shafts of stirrers have been shown. A one-dimensional mathematical model has been considered for describing the rotary velocity field and calculating the hydrodynamic loads used during the design of large-volume precession apparatuses for mixing ore pulps. The validity of the model has been confirmed experimentally using the literature data and a positive experience in the long-term operation of process tanks that are 50–1250 m³ in volume, which are designed for the dealkalization of high concentrations of ore suspensions of noble metals.

Data on the selectivity in the case of infinite dilution obtained using gas–liquid chromatography have been analyzed for seven industrial separating agents. Selective agents for the extractive distillation of binary systems of various types have been recommended.

The refined regularities of the transfer of the momentum and heat in an eddy boundary layer have been given. It has been shown that it is necessary to take them into account in the mathematical modeling of convective heat transfer from the eddy gas flow. The correctness of the proposed method has been verified by comparing the results of computational simulation with experimental data on the convective heat transfer of the eddy air flow.

While the problem of the optimal operation of gas networks has been the object of a large amount of research work, the inclusion of market mechanisms and environmental issues, leading to more complex models resulting in stochastic multiobjective optimization algorithms, has not been considered in its generality. The goal of this article is double: to identify the most general models and algorithms, capable of providing a complete decision support tool, and to show how simplified models can be obtained from them if the complexity of the network makes it necessary to reduce the computational burden.