Systematic controllability analysis for chemical processes

Chapter 6 - Application of stochastic evolutionary optimization techniques to chemical processes

In industrial practice, conceptual process design is typically conducted by repetitive simulation studies, which require detailed design specifications in an early design phase. Guided by heuristics, these iterative solution procedures result in high manual effort and, in addition, no guarantee concerning the quality of the solution can be given. Optimization-based design methods provide a tremendous potential to accelerate and improve conceptual process design. For this purpose, a synthesis framework for the optimization-based design of chemical processes is presented in this thesis. Powerful shortcut and rigorous evaluation methods for reaction and distillation are presented. These methods are computationally efficient in order to allow an optimization-based design of large-scale chemical processes. Various industrial case studies illustrate the application of the novel approaches and highlight their benefits. ...

One of the priority issues in green chemistry is to devise of greener and sustainable chemical strategies and processes to reduce or eliminated the use of hazardous substances. The development of bio-based materials has been constantly increasing, and there is a global effort in agreement with the demands of sustainable management for reducing reliance of fossil resources. In this sense there are green polymerization systems improved to reach increased yields and produce polymers with high molar mass and with specific microstructure characteristics. In the same way, biocatalysis in green chemistry reactions are performer under mild conditions of temperature, pressure and pH. However, along with new materials, chemical processes and reactors must be redesign and developed to guarantee the requirements and meet the demand for green chemical products. Substantial differences with traditional chemical process are based on the synthesis of materials changing the solvents selections and the implementation of continuous reactors, as well as in evaluating the effectiveness of the process through factors such as atom economy, environmental factor, and process mass intensity to implement an industrial green process. This chapter is a review of the processes that have been design and developed to achieve to maximize efficiency and reduce waste.

Simultaneous optimization approach for integrated water-allocation and heat-exchange networks

Conservation of energy usage is essential in chemical process plants due to the expanded energy users and demands alongside the carry-on hike of energy prices. This study analyzed the performance of energy savings in a heat exchanger network (HEN). It is based on decreasing utility usage while increasing process-to-process heat exchange in HEN using a path combination approach at different heat recovery approach temperatures (HRATs). The approach generates different combined path options for heat shifting from utilities to exchangers in a HEN. In terms of cost targeting, the optimal HRAT in a HEN is determined for each path’s combination option. The study focused on the HEN of crude oil preheat trains. Shifting heat load between utilities implies adding and subtracting loads to and from exchangers in a HEN. Therefore, a minor retrofit to compensate for the heat transfer area is required for some HEN exchangers. The optimum HRAT corresponding to the lowest total cost was determined for each option and ranged between 8 °C and 14 °C. Moreover, two out of five options in HEN with low capital investment and a short payback period were found to be promising.

A formal representation of process model equations

The most important task in a chemical process is controlling the process to achieve of process safety, production rates, and product quality. Generally, a chemical plant consists of reaction sections and separation section. Both sections need to control system for the safety and economical in plant. In this research, modification of the separation section of HDA process and the use of heat exchanger network (HEN) to save energy are proposed. The traditional HDA distillation train is replaced by a new design. Furthermore, four new heat exchanger networks (HENs) are developed. The plantwide control structures are designed using the disturbance load propagation method (Wongsri, M., 1990) and heat pathway heuristics (Wongsri, M. and Hermawan Y.D., 2005), respectively. Two kinds of disturbances: thermal and material disturbances are used in evaluation of the plantwide control structures. The performances of the heat integrated plants (HIPs) and the control structures evaluated dynamically by commercial software HYSYS.

A heat exchanger network (HEN) for the process of methanol synthesis has been studied by combination of pinch design method and the application of “Twisted Tube” heat exchanger units as a new technology. The HEN is reconstructed based on the full utilization of maximum allowable pressure drops for the process hot and cold streams. An algorithm is developed to generate design procedure for twisted tube application. The algorithm needs to extend and develop correlation among the pressure drops, heat transfer coefficients, and required surface area through a simple relationship for twisted tube exchangers. It is revealed that a great economic, energy savings and process improvement are realized by using pinch analysis and applying twisted tube units in comparison to existing plants. The paper reveals highly potential benefits of this technology in design and replacement of twisted tube heat exchangers with the conventional shell and tubes type. The HEN is reconstructed by adding 4 new twisted tube units with their detail design in methanol process as a case study. The results show that the application of twisted tube exchangers may achieve significant energy saving for the methanol process with a one-year payback. Moreover, these units may result in a great reduction of carbon emission, operational problems such as heat exchanger fouling, and tube bundle vibration of heat exchangers.

Flexible heat exchanger network design for chemical processes with operation mode changes

This chapter presents the application of the exergy analysis to assess the industrial acetaldehyde production by ethanol partial oxidation. With the conclusions of such assessment, an improved plant configuration is proposed in order to use more efficiently the required process thermal exergy. A model was developed to predict all relevant thermodynamic properties of the process streams. The vapor phase was considered ideal, while the nonideality on liquid phase was corrected by Wilson equation. The exergy analysis indicates that the irreversibilities are mainly produced in the oxidation reactor (458 kW), the waste heat boiler (118 kW), the acetaldehyde absorption tower (105 kW), and distillation tower (247 kW). The improved proposed configuration decreases the overall irreversibility rate to 925 kW, which represents a reduction of 14.7 %. The modifications were made in the absorption and distillation towers and a new heat exchanger network caused a reduction of 28 % in the thermal load of the plant.

Over the years, the design of chemical vapor deposition processes has relied on accumulated empirical ability. It is now well established that the properties of films grown by this chemical process are strongly determined by both transport phenomena and homogeneous and heterogeneous reactions in the reactor. Thermodynamic calculations and mass transport modeling provide a possible approach to delineate the general features of a given process. The application of this concept to the deposition of Si 1-x Ge x alloys has led to a better knowledge of the physicochemical phenomena involved in the growth of these materials

Use of CAPE-OPEN standards in the interoperability between modelling tools (MoT) and process simulators (Simulis ® Thermodynamics and ProSimPlus)

Process engineering (PE) focuses on the design, operation, control and optimization of chemical, physical and biological processes and has applications in many industries. Process intensification (PI) is the key development approach in the modern process engineering. The theory of inventive problem solving (TRIZ) is today considered as the most comprehensive and systematically organized invention knowledge and creative thinking methodology. This paper analyses the opportunities of TRIZ application in PE and especially in combination with PI. In this context the paper outlines the major challenges for TRIZ application in PE, conceptualizes a possible TRIZ-based approach for process intensification and problem solving in PE, and defines the corresponding research agenda. It also presents the results of the original empirical innovation research in the field of solid handling in the ceramic industry, demonstrates a method for identification and prediction of contradictions and introduces the concept of the probability of contradiction occurrence. Additionally, it describes a technique of process mapping that is based on the function and multi-screen analysis of the processes. This technique is illustrated by a case study dealing with granulation process. The research work presented in this paper is a part of the European project “Intensified by Design® platform for the intensification of processes involving solids handling”.

Reactivity of ethylene oxide in contact with basic contaminants

In the chemical process, the uncertainties are always encountered. Therefore, the algorithm of process modeling, simulation, optimization, and control should have the capability to handle the uncertain parameter. Meta-Heuristics Optimization (MO) techniques are attractive global optimization methods inspired by the various industrial phenomena with uncertainty. These methods have been successfully applied to a wide range of chemical engineering problems with a higher level of degree of satisfaction. In this chapter, the authors introduce multiple artificial intelligence techniques: Genetic Algorithm (GA), Biogeography-Based Optimization (BBO), Differential Evolution (DE), Evolutionary Strategy (ES), Probability-Based Incremental Learning (PBIL), Stud Genetic Algorithm (SGA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Fuzzy Logic (FL). It includes the introduction of algorithms and their applications to handle the uncertainty in the chemical process operation.