1,462 research outputs found

    Ant colony optimization for scheduling walking beam reheating furnaces

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a new mathematical model for the walking beam reheating furnace scheduling problem (WBRFSP) in an iron and steel plant, which allows the mixed package of hot and cold slabs and aims to minimize the energy consumption and increase the product quality. An ant colony optimization (ACO) algorithm is designed to solve this model. Simulation results based on the data derived from the field data of an iron and steel plant show the effectiveness of the proposed model and algorithm

    Production Scheduling in Integrated Steel Manufacturing

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    Steel manufacturing is both energy and capital intensive, and it includes multiple production stages, such as iron-making, steelmaking, and rolling. This dissertation investigates the order schedule coordination problem in a multi-stage manufacturing context. A mixed-integer linear programming model is proposed to generate operational (up to the minute) schedules for the steelmaking and rolling stages simultaneously. The proposed multi-stage scheduling model in integrated steel manufacturing can provide a broader view of the cost impact on the individual stages. It also extends the current order scheduling literature in steel manufacturing from a single-stage focus to the coordinated multi-stage focus. Experiments are introduced to study the impact of problem size (number of order batches), order due time and demand pattern on solution performance. Preliminary results from small data instances are reported. A novel heuristic algorithm, Wind Driven Algorithm (WDO), is explained in detail, and numerical parameter study is presented. Another well-known and effective heuristic approach based on Particle Swarm Optimization (PSO) is used as a benchmark for performance comparison. Both algorithms are implemented to solve the scheduling model. Results show that WDO outperforms PSO for the proposed model on solving large sample data instances. Novel contributions and future research areas are highlighted in the conclusion

    Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators

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    This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators’ maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation

    Modeling of opencast mines using Surpac and its optimization

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    In this developing world, the demand for raw materials is increasing at a steady rate, in order to bridge the gap between supply and demand, technological advancement and automation in production method is needed. Since the raw materials are non-replenishable in nature and have took millions of years in their formation, so these resources should be judiciously used with maximum extraction level and aiming for zero mining waste, while adhering to all safety and regulatory norms. In this project, an effort have been made to estimate the resource using Surpac software for ore modeling and optimization algorithm are used for optimizing the shape of the pit and in ultimate pit design to ensure maximum extraction of the deposit. If the available mineral resources are not properly utilized then the cost of production will increase and hence company may lose in this competitive environment. So to ensure that efficient utilization of available resources in terms of shovels and dumpers and other face machinery available, a C++ program have been developed which can dynamically allocate the dumper to the nearest available shovel obeying various constraints to ensure that the production target is reached and the process is fully optimized

    Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process

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    This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI). Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision

    Study on Ground Engineering and Management of Carbonate Oil Field A under Rolling Development Mode

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    Carbonate rock has the characteristics of complicated accumulation rules, large-scale development, high yield but unstable production. Therefore, the management and control of surface engineering projects of carbonate rock oil and gas reservoirs faces huge difficulties and challenges. The construction of surface engineering should conform to the principle of integrated underground and ground construction and adapt to the oilfield development model. This paper takes the newly added area A of the carbonated oil field as an example to study the ground engineering under the rolling development mode and aims to provide the constructive ideas for the surface engineering under rolling development mode. The overall regional process design adheres to the design concept of "environmental protection, efficiency, and innovation", strictly follows the design specifications, and combines reservoir engineering and oil production engineering programs, oil and gas physical properties and chemical composition, product programs, ground natural conditions, etc. According to the technical and economic analysis and comparison of area A, this paper has worked out a suitable surface engineering construction, pipeline network layout and oil and gas gathering and transportation plan for area A. Some auxiliary management recommendations are also proposed in this paper, like sand prevention management and HSE management for carbonate reservoirs

    Demand-side management in industrial sector:A review of heavy industries

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