Ant colony optimization for scheduling walking beam reheating furnaces

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

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

A Module Experimental Process System Development Unit (MEPSDU)

Design work for a photovoltaic module, fabricated using single crystal silicon dendritic web sheet material, resulted in the identification of surface treatment to the module glass superstrate which improved module efficiencies. A final solar module environmental test, a simulated hailstone impact test, was conducted on full size module superstrates to verify that the module's tempered glass superstrate can withstand specified hailstone impacts near the corners and edges of the module. Process sequence design work on the metallization process selective, liquid dopant investigation, dry processing, and antireflective/photoresist application technique tasks, and optimum thickness for Ti/Pd are discussed. A noncontact cleaning method for raw web cleaning was identified and antireflective and photoresist coatings for the dendritic webs were selected. The design of a cell string conveyor, an interconnect feed system, rolling ultrasonic spot bonding heat, and the identification of the optimal commercially available programmable control system are also discussed. An economic analysis to assess cost goals of the process sequence is also given

GA tuning of pitch controller for small scale MAVs

The paper presents the application of intelligent tuning methods for the control of a prototype MAV in order to address problems associated with bandwidth limited actuators and gust alleviation. Specifically, as a proof of concept, the investigation is focused on the pitch control of a MAV. The work is supported by experimental results from wind tunnel testing that shows the merits of the use of Genetic Algorithm (GA) tuning techniques compared to classical, empirical tuning methodologies. To provide a measure of relative merit, the controller responses are evaluated using the ITAE performance index. In this way, the proposed method is shown to induce far superior dynamic performance compared to traditional approaches

NASA patent abstracts bibliography: A continuing bibliography. Section 1: Abstracts (supplement 29)

Abstracts are provided for 115 patents and patent applications entered into the NASA scientific and technical information system during the period January 1986 through June 1986. Each entry consists of a citation, an abstract, and in most cases, a key illustration selected from the patent application

Function value-based multi-objective optimisation of reheating furnace operations using Hooke-Jeeves algorithm

Improved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO2 emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature

A scheduling model for production in a hot strip mill

M.Ing.This research dissertation highlights the important role of scheduling in a production environment. The functioning of an integrated iron and steel works is discussed. The importance of production scheduling in this environment is shown, followed by a literature survey of strip mill production scheduling models. Thereafter a model is introduced that aids in the production scheduling of plate via coil in a hot strip mill. Finally the benefits of the scheduling model are shown

Model-based multi-objective optimisation of reheating furnace operations using genetic algorithm

An effective optimisation strategy for metal reheating processes is crucial for the economic operation of the furnace while supplying products of a consistent quality. An optimum reheating process may be defined as one which produces heated stock to a desired discharge temperature and temperature uniformity while consuming minimum amount of fuel energy. A strategic framework to solve this multi-objective optimisation problem for a large-scale reheating furnace is presented in this paper. For a given production condition, a model-based multi-objective optimisation strategy using genetic algorithm was adopted to determine an optimal temperature trajectory of the bloom so as to minimise an appropriate cost function. Definition of the cost function has been facilitated by a set of fuzzy rules which is easily adaptable to different trade-offs between the bloom desired discharge temperature, temperature uniformity and specific fuel consumption. A number of scenarios with respect to these trade-offs were evaluated and the results suggested that the developed furnace model was able to provide insight into the dynamic heating behaviour with respect to the multi-objective criteria. Suggest findings that current furnace practice places more emphasis on heated product quality than energy efficiency