INTEGRATED PRODUCTION FOR OIL REFINERIES AND PETROCHEMICAL PLANTS

In an increasingly globalised commodity market and under continually changing economic scenarios, oil, gas and petrochemical plants are forced to improve their operation practices in order to remain competitive. One strategy that can be adopted is to exploit the synergy between oil refineries and petrochemical plants through the strategy of integrated production. In this work, issues of integrated production strategy with respect to profitability, implementation and flexibility are explored. Profitability is the key motivation for any plant to change its operation practices. Three options for the strategy of integrated production are considered: integration of final products, integration of intermediate products, and integration of processing units. Decisions are made on the allocation of material resources, the distribution of products and the operating conditions of process units. These decisions are optimised for maximum profit while satisfying all production constraints. In the integrated production of an oil refinery and a petrochemical plant, propylene, naphtha, gasoil and pygas are selected for integration. The benefits of the integrated production strategy are lower costs and higher profits to the integrated plants. Systematic implementation of integrated production strategy is carried out by evaluating the necessary condition and generating an interaction model to bridge information flow between the two plants. Sensitivity analysis is used to evaluate the necessary condition for integrated production. The interaction model regulates the required information !low between the two plants and screens for options of integrated production network. Flexibility of integrated production plan is studied by varying demands and prices of exchanged materials. For an integrated production plant to be flexible, it has to remain feasible even when these parameters change. Flexibility analysis allows steps to be carried out at an early stage to ensure feasibility of the integrated production plan. All integrated production planning problems are formulated as nonlinear programming problem (NLP) and solved using the modular sequential optimisation approach. Case studies are performed to demonstrate how the three issues are addressed

Carbon emission reduction targeting through process integration and fuel switching with mathematical modeling

Carbon emission reduction targeting is an important and effective effort for industry to contribute in controlling greenhouse gases concentration in atmosphere. Graphical approach has been proposed for CO2 emissions reduction targeting via HEN retrofit and fuel switching. However, it involves potentially time consuming manual procedures and the quality of solutions produced greatly depends on designer's experience and judgment. Besides, graphical approach hardly account for the cost factor during the design phase, thus potentially generate complex design. This paper introduces an MINLP model for simultaneous CO2 emissions reduction targeting via fuel switching and HEN retrofit. A sequential model execution was proposed along with the proposed model. The application of the model on a crude preheat train case study has demonstrated its workability to generate optimal solution for targeted CO2 emissions reduction at minimum payback period

Niching grey wolf optimizer for multimodal optimization problems

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly

A new approach for strength and stiffness prediction of discontinuous fibre reinforced composites (DFC)

A new modelling methodology for strength and stiffness prediction of discontinuous fibre-reinforced composites (DFC) is proposed. This has been validated for both thermoplastic and thermoset, prepreg based, carbon fibre reinforced, random DFC laminates having high volume fraction, by implementing it in a commercial FE solver. The methodology involves explicit generation of internal architecture of DFC through an algorithm which is efficient (faster model generation and solution), easily customizable and scalable. It captures many of the realistic features of the DFC such as variation in volume fraction, interlacing of strands, random orientation and thickness variation of strands. Thus, the model accounts for the natural mechanical property variation, which is characteristic of random DFCs and was found to be conservative in terms of prediction of tensile strength and stiffness for all the validation cases considered. It is generic in the sense that it can be easily extended to generate preferentially aligned and hybrid DFC laminates.Universiti Teknologi PETRONA

4E analysis of a two-stage refrigeration system through surrogate models based on response surface methods and hybrid grey wolf optimizer

Refrigeration systems are complex, non-linear, multi-modal, and multi-dimensional. However, traditional methods are based on a trial and error process to optimize these systems, and a global optimum operating point cannot be guaranteed. Therefore, this work aims to study a two-stage vapor compression refrigeration system (VCRS) through a novel and robust hybrid multi-objective grey wolf optimizer (HMOGWO) algorithm. The system is modeled using response surface methods (RSM) to investigate the impacts of design variables on the set responses. Firstly, the interaction between the system components and their cycle behavior is analyzed by building four surrogate models using RSM. The model fit statistics indicate that they are statistically significant and agree with the design data. Three conflicting scenarios in bi-objective optimization are built focusing on the overall system following the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP) decision-making methods. The optimal solutions indicate that for the first to third scenarios, the exergetic efficiency (EE) and capital expenditure (CAPEX) are optimized by 33.4% and 7.5%, and the EE and operational expenditure (OPEX) are improved by 27.4% and 19.0%. The EE and global warming potential (GWP) are also optimized by 27.2% and 19.1%, where the proposed HMOGWO outperforms the MOGWO and NSGA-II. Finally, the K-means clustering technique is applied for Pareto characterization. Based on the research outcomes, the combined RSM and HMOGWO techniques have proved an excellent solution to simulate and optimize two-stage VCRS

Parametric Optimization of a Two Stage Vapor Compression Refrigeration System by Comparative Evolutionary Techniques

Multistage refrigeration system plays a vital role in industrial refrigeration for the chemical, petrochemical, pharmaceuticals and food industries. Modern chemical industries are complex, and the problems are commonly multi-dimensional, non-linear and time-consuming. This study presents the application of evolutionary computation techniques, namely PSO (particle swarm optimization), GA (Genetic Algorithm) and SA (Simulated Annealing) to solve a design problem of a two-stage vapor compression refrigeration system. Two objectives are evaluated, namely the minimization of total energy consumption and maximization of the coefficient of performance (COP) of the system. The basis of design for the two-stage refrigeration system is built from and validated against data from published literature. The mass flow ratio, evaporator and condenser temperature, parameters for subcooling and desuperheating, and the coefficient of performance for the basis of design show acceptable results. The errors are below 5% against the data from published literature, which are within errors of significant figures in the calculations. In this work, the optimum solutions show a reduction of the required amount of energy consumption by 30.8% and an increase of the COP by nearly 77% with respect to the basis of design. Further improvements are made to the optimization procedures to prevent early convergence and to increase the search efficiency for finding the global optima. The findings by PSO, GA and SA are in agreement, and all evolutionary techniques achieved proper convergence of the two objective functions. It is also found that PSO requires lower computational effort, less computation time and is also easier to implement compared to GA and SA

Exergy Analysis For Fuel Reduction Strategies In Crude Distillation Unit

Inefficient furnaces and heat exchangers contribute to the depletion of fossil fuel problem due to higher fuel demand and higher carbon emission. The method of exergy analysis is applied to the furnace and crude preheat train (CPT) in a crude distillation unit (CDU) to determine performance benchmark of the system. This paper presents exergy analysis and strategies to reduce exergy loss through process modification. The highest exergy loss was found to be located at the inlet furnace. The proposed options for fuel reduction strategies are reduction of heat loss from furnace stack and overall cleaning schedule of CPT. The feasibility and economic analysis for both options are investigated. From the results, overall cleaning schedule of CPT contributes to the highest energy saving of 5.6%. However, reduction of heat loss from furnace stack is the highest cost saving by about 6.4%

Profit optimization for chemical process plant based on a probabilistic approach by incorporating material flow uncertainties

This paper reports how the economic performance of a chemical process plant is affected by material flow uncertainties from the plant inlet and outlet. Two chance-constrained optimization models were proposed. The models were tested using case studies of an existing gas processing plant. Profit optimization for the case studies was made with respect to the reliability of holding the process constraints at a certain confidence level [0.5, 1]. The optimal profit change for uncertainty from the plant inlet within the confidence interval [0.96, 1] was 86%. On the other hand, the optimal profit change for uncertainty from the plant outlet was only 2% for the same confidence level interval considered. This suggests that the uncertainty from the plant inlet has a major impact on the overall economic performance of the plant. Sensitivity analysis showed how uncertain parameters from both plant sides can affect the overall profit significantly

Modeling of Multiple Reversible Reaction System

AbstractGrowing world population increases food demand. Reaction coupling between ammonia and urea synthesis provides insight in developing fertilizer production plant which is more energy efficient and which in turn enables abundant and cheap food production. The ammonia-urea reaction coupling is still a new field and requires simple and sufficient models to describe the system. Mathematical models derivation, simulation, constants tuning and validations were done using commercial software. Sufficient models were obtained for the future development of reaction coupling conceptual reactor. For urea synthesis reaction, the equilibrium carbon dioxide conversion is close to 80% and the equilibrium urea concentration is around 750 mol/m3 at the optimum operating conditions of around 450K and 12 MPa. The optimum range of NH3:CO2 feed ratio is 2-3.5. The equilibrium nitrogen conversion of circa 27% and the equilibrium ammonia concentration of 300 mol/m3 are obtained for ammonia synthesis at the optimum operating conditions of around 660K and 23 MPa. The average percent error for nitrogen equilibrium conversion and ammonia equilibrium molar flow is only 6.154% and 5.932% respectively compared to published data [22]