Petrochemical Industry: Assessment and Planning Using Multicriteria Decision Aid Methods (original) (raw)

Decision-making for Petrochemical Planning Using Multiobjective and Strategic Tools

Chemical Engineering Research and Design, 2006

ecision-making for planning a petrochemical industry is a difficult task, particularly when decisions are required to be made under constraints and different objectives. This paper presents the application of multiobjective optimization tools for planning of a mixed-integer model of a petrochemical industry to arrive at a small set of good solutions out of the Pareto optimal solutions. The two main objectives are economic gain and risk from plant accidents. Following this optimization, an economical strategic tool is used to reach the final decision. The proposed procedure has been applied to the petrochemical industry in Kuwait and found to be successful in defining a balanced petrochemical network with acceptable risk.

A Mathematical Programming Model for Optimum Economic Planning of the Saudi Arabian Petrochemical Industry

A new mixed integer linear programming model is formulated and used to model the development of the petrochemical industry in Saudi Arabia. The proposed model features a new mathematical programming formulation, new products and processes, new variables and constraints, and more accurate estimates of production costs based on local conditions. The products considered in the model are classified into four main categories: aromatics, ethylene derivatives, propylene derivatives, and synthetic gas derivatives. The model is used to recommend petrochemical products, their respective capacities, and the corresponding production technologies. Utilization of results is discussed and sensitivity analysis is performed.

Planning an Integrated Petrochemical Industry with an Environmental Objective

Industrial & Engineering Chemistry Research, 2001

Production planning in the petrochemical industry requires a model that can account for the different interactions, needs, and features and provide at the same time suitable mathematical representation. In this work, a model with an environmental objective is presented. The system is formulated as a mixed-integer linear programming model where new value-added products are produced from the basic feedstock chemicals. From the superstructure of the technology alternatives, the optimal set of processes is selected with the objective function of sustainability. The quest for pollution prevention and increased pressure and demand for environmental considerations makes sustainability an important objective function. In this study, sustainability is quantified by a health index of the chemicals and increasing profit represented by processadded value. The model is applied to the case study of planning the development of the Kuwait petrochemical industry. Results give an optimal structure for the development and prove that simple indicators can represent sustainability, giving good results in selecting environmentally friendly processes and at the same time being profitable.

An optimization model for guiding the petrochemical industry development in Saudi Arabia

Engineering Optimization, 2002

A mixed integer linear programming model is formulated for determining the optimum plan for the expansion of the Saudi Arabian petrochemical industry. The products selected for consideration fall into four categories: propylene derivatives, ethylene derivatives, synthesis gas derivatives, and aromatic derivatives. The model incorporates new variables and constraints, and realistic estimates of production costs, which are calculated based on local conditions in Saudi Arabia. For each production process, the unit production cost is assumed to be a function of production capacity. The input data for each product includes relevant production technologies, capacities, local production costs, and selling price. The solution of the model gives the recommended products under different scenarios of available capital investment and feedstock. The results are reported and analyzed.

Multiobjective analysis in modeling the petrochemical industry

Chemical Engineering Science, 1980

The StNCtUIXkg of the petrochenucal mdustry has been considered both as a s&e obJectwe and as a multiobJectwe problem Three obJectlve functions have been consldered The maxumzation of the thermodynamic avadabdlty change, the rnuumwat~on of the lost work and the muunuzatlon of the feedstock consumption The tist two ObJeCtlveS aim at structunng the rndustry for "optlmdm" energy utihzatlon, wfule the ttnrd aims at the optimum utdnation of raw matenals The smgle otqectzve analysis provided three optimum structures that constltute bounds of the actual performance of the petrochemtcal Industry The mu&lobJectlve analysts on the other hand provides the process designer a set of alternative solutions and SUbJeCtWe cnterla have to be used m order to select the "best"

Optimum multi-plant, multi-supplier production planning for multi-grade petrochemicals

Engineering Optimization, 2009

A mixed-integer linear programming model is presented for the optimum planning of multi-plant, multisupplier, and multi-grade petrochemical production. In the production of multiple grades of a given petrochemical product, the amount of transitional off-spec production depends on the sequencing of different grades. For each time period, the discrete-time model determines the optimum mix of petrochemical grades for each plant, the quantity to produce of each selected grade, and the optimum production sequence of different grades. In addition, assuming limited raw-material availability, the model determines the quantity of each raw material to purchase from each supplier. The model incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The model is applied to real-life data from multi-grade polypropylene production in a large petrochemical company.

A multi-criteria decision framework for unstructured complex problem: a strategy for biofuel production

A Multi-Criteria Decision Analysis (MCDA) approach was designed and used to evaluate different Fast Pyrolysis Unit (FPU) sizes. The MCDA approach is implemented via two models: Excel worksheet and automated model via Logical Decision® software. The proposed MCDA approach is an integration of the Pugh Concept Selection Matrix, Weighting Sum Method (WSM), and sensitivity analysis using Logical Decision® software. The data for the problem was collected from ten Subject Matter Experts (SMEs) using Pugh Matrix. In addition, two other integrated MCDA approaches were used to solve the same problem. The first approach integrated the Pugh Matrix and WSM. The second approach integrated the Pugh Matrix and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The designed framework is presented to identify Biofuel Production Stakeholders (BPS), their perspectives, and their requirements. The small FPU was found to be the best alternative using the three approaches. Furthermore, all these approaches allowed ranking of different alternatives based on the five perspectives of manufacturing biofuel production units: economic, environmental, technical, legal, and social perspectives. These five perspectives rely on 18 requirements that were frequently mentioned in previous research. The use of each approach gave different insight about the problem which could help decision-makers to understand the problem better and discuss the alternatives in depth. Sensitivity analysis suggested that the medium FPU is the best alternative in specific conditions under the perspectives-level analysis. On the other hand, it was suggested that the large FPU is the best alternative under specific conditions at the requirements-level analysis. An interesting finding from this research is that from the environmental perspective the x medium FPU is recommended as the best alternative instead of the small FPU. In addition, the TOPSIS analysis provided the theoretical positive and negative ideal solutions to help the decision makers gain a better perception of the optimal design of FPUs. Moreover, WSM was found to be the simplest MCDA tool to use. In contrast, TOPSIS was found to be a more complicated tool yet similar to WSM both could not examine result robustness. The proposed approach provided the result robustness limitations.