Design Of Chemical Plants Research Articles

A projection of commercial-scale organic photovoltaic module costs

Organic photovoltaics (OPVs) are a recent technology that has gained much attention as a potential low cost power source. Despite this promise, there is a lack of published studies that address the likely cost of commercial-scale OPV modules. In this work, an engineering study estimate has been performed to determine the projected cost of mass-manufactured OPV modules. The materials, production capital and operating costs have been calculated and sensitivity analyses performed to determine the parameters of greatest economic influence. Significantly, the model includes a calculation of the costs required to establish bulk manufacturing of the current high cost speciality materials components, encompassing synthesis and associated chemical plant design. The economic modelling reveals that the calculated mass-manufactured OPV module costs are considerably lower than current literature estimates, with OPV modules costed at $7.85 per square metre with an uncertainty of±30%. Total module cost was found to be most sensitive to the plastic substrate prices, while the production rate did not have a significant impact on module cost for rates above~50 m 2 /min. The results highlight the future cost potential of OPV technology and can be used to assist with scale-up planning. • Here we present an engineering study estimate of the projected cost of OPV. • Materials, production capital and operating costs have been calculated. • Sensitivity analysis has determined the parameters of greatest economic influence. • Projected cost of large scale manufacture of OPV modules is $7.85±2.35/m 2 . • The PET substrate is found to be the major cost in module fabrication.

Thermal Conductivity of [C4mim][(CF3SO2)2N] and [C2mim][EtSO4] and Their IoNanofluids with Carbon Nanotubes: Experiment and Theory

Measurements of the thermal conductivity of ionic liquids are extremely important for current chemical plant design of new environmentally safe processes. Existing data are very scarce and inaccurate. IoNanofluids have emerged as a possible alternative to current engineering fluids for heat transfer applications, namely in small volume heat exchangers. In the present paper we report new data on the thermal conductivity of of 1-n-butyl-3-methylimidazolium bis(trifluoromethanesulfonylimide ([C4mim][(CF3SO2)2N]) and 1-ethyl-3-methylimidazolium ethylsulfate ([C2mim][EtSO4]) at temperatures between (293 and 343) K and IoNanofluids with multiwalled carbon nanotubes based on them, to understand the effect of adding nanomaterials to a ionic liquid matrix and its effect on the mentioned thermal property. The application of existing models to predict the behavior of the IoNanofluids, namely, the enhancement in the thermal conductivity, showed that it is fundamental to understand better the mechanism of heat transfe...

유기물의 자연발화점 예측을 위한 부분최소자승법과 SVM의 비교

The autoignition temperature is one of the most important physical properties used to determine the flammability characteristics of chemical substances. Despite the needs of the experimental autoignition temperature data for the design of chemical plants, it is not easy to get the data. This study have built and compared partial least squares (PLS) and support vector machine (SVM) models to predict the autoignition temperatures of 503 organic compounds out of DIPPR 801. As the independent variables of the models, 59 functional groups were chosen based on the group contribution method. The prediction errors calculated from cross-validation were employed to determine the optimal parameters of two models. And, particle swarm optimization was used to get three parameters of SVM model. The PLS and SVM results of the average absolute errors for the whole data range from 58.59K and 29.11K, respectively, indicating that the predictive ability of the SVM is much superior than PLS.

Economic and Environmental Assessment of Alternatives to the Extraction of Acetic Acid from Water

A comparison was made between conventional binary distillation and four feasible alternatives flowsheets to separate acetic acid from water attending to both economic and environmental criteria. All extraction steps use diethyl ether as an extractant. Our goals were, first, to estimate the economic and environmental incentives for each of the alternatives proposed in order to understand the trade-offs associated with each system and second, to show how, in some flowsheets, it is possible to rigorously decompose the system and perform detailed optimization without the necessity of simultaneous optimization of all the equipment implied in the flowsheet. To carry out the economic evaluation, two simple criteria were used: Economic Potential and Total Annual Cost. These parameters were chosen because they can be used at various stages in the chemical plant design without the necessity of a complete picture of the industrial process. The environmental impacts are measured through the Ecoindicator-99 methodology, which reflects the advances in the damaged-oriented methodology recently developed for Life Cycle Assessment. For each of the alternatives, detailed optimization was performed to determine the Pareto’s individual curves using the ε-constraint method. With the individual Pareto curves, it is also possible to obtain the compound Pareto’s curve, and to then superimpose the individual Pareto’s curves associated with the five alternatives to identify the trade-offs of this multiobjective optimization and ultimately determine the best alternatives, and even their optimum operational conditions.

AN OPEN SOURCE EXERGY CALCULATOR TOOL

The constant tightening of environmental regulations and the ongoing need to reduce operating costs have posed a challenge for the design of any chemical process. Exergy is a potential indicator that can aid in the design of energy efficient chemical processes and plants. An open source exergy calculator has been created by embedding the calculation procedure in an open source chemical process simulator. This serves as a potential tool for design teams to quickly evaluate several process options in detail in order to understand their energy utilization. A simulation case study is presented to show the application of the tool.

AN OVERVIEW OF CHEMICAL PROCESS DESIGN ENGINEERING

The nature of Chemical Process Design Engineering requires that it utilize methods of design that sometimes differ from product design, yet clearly, many overlaps exist. This paper describes the general procedure for designing a chemical plant, the common design documents, and today’s tools for achieving a chemical plant design. Parallels and differences between process and product design are highlighted.

A combined heuristic and indicator-based methodology for design of sustainable chemical process plants

The current emphasis on sustainable production has prompted chemical plants to minimize raw material and energy usage without compromising on economics. While computer tools are available to assist in sustainability assessment, their applications are constrained to a specific domain of the design synthesis problem. This paper outlines a design synthesis strategy that integrates two computer methodologies – ENVOP Expert and SustainPro – for simultaneous generation, analysis, evaluation, and optimization of sustainable process alternatives. ENVOP Expert diagnoses waste sources, identifies alternatives, and highlights trade-offs between environmental and economic objectives. This is complemented by SustainPro which evaluates the alternatives and screens them in-depth through indicators for profit and energy, water, and raw material usage. This results in accurate identification of the root causes, comprehensive generation of design alternatives, and effective reduction of the optimization search space. The framework is illustrated using an acetone process and a methanol and dimethyl ether production case study.

マイクロ化学プロセスの設計と操作

Micro devices have large specific surface area. This feature can be effectively used to control the residence time and the temperature in the devices. In the manuscript, the feature of the design and operation problems of micro chemical plant is discussed. First, it is explained that the temperature and residence time can be controlled precisely in micro devices by using examples. Then, the importance of optimal shape design is stressed using a case study of exothermic reaction.In most cases, the flow rate of single channel is not enough for real production. The numbering-up is dominant method for micro plant to increase the flow rate. The feature of several numbering-up method is explained and the desirable numbering-up procedure is proposed. The blockage of the channels is a dominant abnormal condition for micro devices. Therefore, to achieve the stable long-term operation of micro chemical plants it is very important to detect the blockage of the channels as early as possible. It is not practical to install the sensors in all the parallel channels from the standpoint of measuring device cost. A distributer structure that can detect and diagnose a blocked channel has been proposed. In the proposed system, two flow sensors are enough for detecting the blocked channel even when the process consists of 4 to 8 parallel channels. Furthermore, a flow control system that ensures the steady state operation for each of the parallel devices during the replacement of the device in the blocked channel is proposed.

MULTICRITERIA OPTIMIZATION OF MULTIPRODUCT BATCH CHEMICAL PROCESS USING GENETIC ALGORITHM

ABSTRACT Optimal design problems are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is first formulated as a multiobjective optimization problem, to be solved using the well‐suited nondominating sorting genetic algorithm (NSGA‐II). The NSGA‐II has the capability to achieve fine tuning of variables in determining a set of nondominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA‐II ability to identify a set of optimal solutions provides the decision maker with a complete picture of the optimal solution space to gain better and appropriate choices. The effectiveness of NSGA‐II method with multiobjective optimization problem is illustrated through a carefully referenced example. PRACTICAL APPLICATIONSThe inherent dynamic nature of batch processes allows for their ability to handle variations in feedstock and product specifications, and provides the flexibility required for multiproduct or multipurpose facilities. They are thus best suited for the manufacture of low‐volume, high‐value products, such as specialty chemicals, pharmaceuticals, agricultural, food and consumer products, and most recently the constantly growing spectrum of biotechnology‐enabled products. Batch processing in the food industry has some unit operations, such as panning, whipping (emulsifying), and the pulling of taffy, which are not too often used in other industries, as well as other operations,such as blending, tabletting (including granulation) and lyophilization, which are common in other industries. Reduced time to market, lower production costs and improved flexibility are all critical success factors for batch processes.

Degrees of freedom analysis for a distillation column

Precise determination of the number of degrees of freedom is of great significance in the design, operation, renovation, and optimization of chemical plants. Two fundamental works devoted to the determination of the number of degrees of freedom for chemical technology processes are considered, and the basic distinctions between the two approaches are discussed. It is explained why the numbers of degrees of freedom determined for the similar chemical engineering objects considered in those works are different.

A knowledge-based simulation-optimization framework and system for sustainable process operations

Design and operation of chemical plants involves a combination of synthesis, analysis and evaluation of alternatives. Such activities have traditionally been driven by economic factors first, followed by engineering, safety and environmental considerations. Recently, chemical companies have embraced the concept of sustainable development, entailing renewable feed materials and energy, non-toxic and biodegradable products, and waste minimization or even elimination at source. In this paper, we introduce a knowledge-based simulation-optimization framework for generating sustainable alternatives to chemical processes. The framework has been developed by combining different process systems engineering methodologies – the knowledge-based approach for identifying the root cause of waste generation, the hierarchical design method for generating alternative designs, sustainability metrics, and multi-objective optimization – into one coherent simulation-optimization framework. This is implemented as a decision-support system using Gensym's G2 and the HYSYS process simulator. We illustrate the framework and system using the HDA and biodiesel production case studies.

Search for Optimal Design of Multiproduct Batch Plants under Uncertain Demand using Gaussian Process Modeling Solved by Heuristics Methods

This work deals with the problem of the search for optimal design of multiproduct batch chemical plants found in a chemical engineering process with uncertain demand. The aim of this work is to minimize the investment cost and find out the number and size of parallel equipment units in each stage. For this purpose, it is proposed to solve the problem in two different ways: the first way is by using Monte Carlo Method (MC) and the second way is by Genetics Algorithms (GAs). This GAs consider an effective mixed continuous discrete coding method with a four- point crossover operator, which take into account simultaneously, the uncertainty on the demand using Gaussian process modeling with two criteria maximization the Net Present Value (NPV) and Flexibility Index (FI). The results (number and size of equipment, investment cost, production time (Hi), NPV, FI, CPU time and Idle times in plant) obtained by GAs are better than the MC. This methodology can help the decision makers and constitutes a very promising framework for finding a set of "good solutions."

Optimal Solutions of Multiproduct Batch Chemical Process Using Multiobjective Genetic Algorithm with Expert Decision System

Optimal design problem are widely known by their multiple performance measures that are often competing with each other. In this paper, an optimal multiproduct batch chemical plant design is presented. The design is firstly formulated as a multiobjective optimization problem, to be solved using the well suited non dominating sorting genetic algorithm (NSGA-II). The NSGA-II have capability to achieve fine tuning of variables in determining a set of non dominating solutions distributed along the Pareto front in a single run of the algorithm. The NSGA-II ability to identify a set of optimal solutions provides the decision-maker DM with a complete picture of the optimal solution space to gain better and appropriate choices. Then an outranking with PROMETHEE II helps the decision-maker to finalize the selection of a best compromise. The effectiveness of NSGA-II method with multiojective optimization problem is illustrated through two carefully referenced examples.

An exergy calculator tool for process simulation

AbstractThe constant tightening of environmental regulations and the ongoing need to reduce operating costs have posed a challenge for the design of any chemical process. Process engineers use process simulators to help them perform calculations that will, ultimately, result in design parameters or operating conditions for a plant or process. Exergy is a potential indicator that can aid in the design of energy efficient chemical processes and plants. The exergy concept has been increasingly used as a tool to locate the critical energy use in many industrial processes, both chemical and non‐chemical. However, currently most process simulators in the market do not offer the capability of calculating the exergy of a process. An open‐source exergy calculator has been created by embedding the calculation procedure in an open‐source chemical process simulator. This improves process simulation by including a potential tool for design teams to quickly evaluate several process options in detail in order to understand their energy utilisation. A simple exergy analysis for a gas processing facility is used to demonstrate the capabilities of the tool. The analysis shows where the largest quantities of exergy are being consumed within the plant, thus pointing to areas where improvement in energy usage can be made. The use of exergy as a potential design and retrofit tool is also discussed. Copyright © 2007 Curtin University of Technology and John Wiley & Sons, Ltd.

Sulfuric acid manufacture: analysis, control and optimization

By some measure the most widely produced chemical in the world today, sulfuric acid has an extraordinary range of modern uses, including phosphate fertilizer production, explosives, glue, wood preservative and lead-acid batteries. An exceptionally corrosive and dangerous acid, production of sulfuric acid requires stringent adherence to environmental regulatory guidance within cost-efficient standards of production. This work provides an experience-based review of how sulfuric acid plants work, how they should be designed and how they should be operated for maximum sulfur capture and minimum environmental impact. Using a combination of practical experience and deep physical analysis, Davenport and King review sulfur manufacturing in the contemporary world where regulatory guidance is becoming ever tighter (and where new processes are being required to meet them), and where water consumption and energy considerations are being brought to bear on sulfuric acid plant operations. This 2e will examine in particular newly developed acid-making processes and new methods of minimizing unwanted sulfur emissions. The target readers are recently graduated science and engineering students who are entering the chemical industry and experienced professionals within chemical plant design companies, chemical plant production companies, sulfuric acid recycling companies and sulfuric acid users. They will use the book to design, control, optimize and operate sulfuric acid plants around the world. Unique mathematical analysis of sulfuric acid manufacturing processes, providing a sound basis for optimizing sulfuric acid manufacturing processes. Analysis of recently developed sulfuric acid manufacturing techniques suggests advantages and disadvantages of the new processes from the energy and environmental points of view. Analysis of tail gas sulfur capture processes indicates the best way to combine sulfuric acid making and tailgas sulfur-capture processes from the energy and environmental points of view. Draws on industrial connections of the authors through years of hands-on experience in sulfuric acid manufacture.

Optimal design of multiproduct batch chemical plant using NSGA‐II

AbstractThe optimal design of a multiproduct batch chemical plant is formulated as a multiobjective optimization problem, and the resulting constrained mixed‐integer nonlinear program (MINLP) is solved by the nondominated sorting genetic algorithm approach (NSGA‐II). By putting bounds on the objective function values, the constrained MINLP problem can be solved efficiently by NSGA‐II to generate a set of feasible nondominated solutions in the range desired by the decision‐maker in a single run of the algorithm. The evolution of the entire set of nondominated solutions helps the decision‐maker to make a better choice of the appropriate design from among several alternatives. The large set of solutions also provides a rich source of excellent initial guesses for solution of the same problem by alternative approaches to achieve any specific target for the objective functions. Copyright © 2006 Curtin University of Technology and John Wiley & Sons, Ltd.

Development of a Methodology for Assessing Inherent Occupational Health Hazards

In the preliminary stages of chemical plant design, selecting the chemical process route is one of the main design decisions. Previously, the most important factor in selecting the best chemical process route was economics. Now safety, environmental and occupational health issues have become important considerations. Health risks to workers could be reduced by better selection of the chemical process route during the initial stages of process design. The chemical process route may be defined as the raw material(s) and the sequence of reactions that converts them to the desired product(s). In order to choose the ‘healthiest’ one from a number of alternative routes, the potential health hazards must be quantified. Ranking of alternative chemical process routes based on the severity of potential health effects to the workers exposed could provide an assessment method for avoiding potential harm to humans. The Process Route Healthiness Index (PRHI) has been developed to quantify the health hazards that might arise from chemical processes; the higher the index, the higher the hazards. The PRHI is influenced by the health impacts due to potential chemical releases and the concentration of airborne chemicals inhaled by workers. The index has been applied to six alternative routes to methyl methacrylate (MMA). The resulting ranking is compared to those obtained from an Inherent Safety Index, an Environmental Hazard Index and production cost estimates for the same chemical process routes.

An option generation and selection methodology for process equipment selection

The option generation and selection (OGS) methodology forms part of a general approach for the design of agile chemical plants based on business, product and process knowledge, with support from information models. This paper describes an equipment OGS tool that encompasses the principles of combinatorial process and plant design. The main components of the methodology are: an equipment option generation model described as a set of objects, and the net relationships between them, and an equipment option selection model which consists of procedures for equipment selection. The two models are supported by databases containing information specific to each equipment type, the concept on which the equipment is based, and relationships with other equipment types. Robust, systematic and complete forms of these models can be used as the basis of an expert system for process equipment design, with equipment selected using these tools satisfying the requirements of both specific processes and families of processes (that contain common features, similar functional groups or similar raw material requirements for process operations). Application of the methodology also allows the evaluation of options for reconfiguring existing plant.

Ullmann's chemical engineering and plant design: v.1: Mathematics and physics in chemical engineering, fundamentals of chemical engineering; v.2: Plant and process design

Ullmann's chemical engineering and plant design: v.1: Mathematics and physics in chemical engineering, fundamentals of chemical engineering; v.2: Plant and process design

Design of Pipeless Chemical Batch Plants with Queueing Networks

The problem of designing pipeless batch plants is tackled using a multiclass re-entrant queueing network model. The overall design procedure is divided into three hierarchical phases: the preliminary phase, neighbor search algorithm (NSA), and greedy mean value analysis (GMVA). In the preliminary phase, the types of products and stations, as well as the capacities of equipment items, are determined. GMVA, which is a generalization of conventional MVA, finds the optimal job constitution for the system within a short computer processing unit (CPU) time, after the station configuration is fixed, whereas the station configuration is successively improved by NSA. The moderate time complexity of the proposed method extends the range of applications to practical-sized problems. An alternative design approach that is based on the decomposition approximation and formulated as a mixed integer nonlinear programming (MINLP) problem is also developed. The effectiveness of the method is illustrated through the examples of medium-sized pipeless plant design problems. Simulation studies using a commercial simulation package are described and observed to support the validity of the analytic method.