Cemalettin Kubat

FUZZYFCC: Fuzzy logic control of a fluid catalytic cracking unit (FCCU) to improve dynamic performance

Abstract In this paper, fuzzy logic control of a fluid catalytic cracking unit (FCCU) is proposed. Fluid catalytic cracking (FCC) process is a unit that converts heavy distillates like gas oil or residues to gasoline and middle distillates using cracking catalyst. About 45% of worldwide gasoline production comes from FCC processes and its ancillary units. Since a typical FCC unit can process a large amount of the feedstock into more valuable products, the overall economic benefits of a refining could be considerably increased if proper control and optimization strategies are implemented. FCC processes are known to be very difficult to model and control because of the large process scale, complicated hydro-dynamics and complex kinetics of both cracking and coke burning reactions. One of the more heavily investigated terms of nonlinear control, in the field of “intelligent control”, is that due to fuzzy logic controllers (FLCs). FLCs have been successfully applied to a stream of difficult, nonlinear dynamical process such as FCC. Here, with an application to a Turkish refinery FCC unit of FLC fuzzy results obtained using Matlab-Fuzzy Logic Toolbox version 6.5 were found to be acceptable. The paper indicates how fuzzy logic control (FLC), as a promising control technique, would be effectively used for improved process control of FCC in refinery process industry.

Bofy-fuzzy logic control for the basic oxygen furnace (BOF)

Abstract In this paper, fuzzy modeling for the control of basic oxygen furnace (BOF) processes is proposed. BOF is a widely preferred and effective steel making method due to its higher productivity and considerably low production cost. Therefore, today almost 65% of the total crude steel production in the world is met by using the BOF method. Higher steel output at lower cost is one of the main objectives of modern steel making methods. In order to accomplish this objective, fuzzy modeling was employed in this study in order to control some variables related to the BOF process. Fuzzy modeling and control in BOF promise a solution to the strongly non-linear problems associated with the process, which have so far proven extremely difficult to be solved by conventional control methods. Data set was selected as inputs from the real empirical BOF data in an integrated steel plant based in Turkey. Although there were negligible deviations from the target values, most of the fuzzy results obtained using MATLAB-Fuzzy Logic Toolbox version 5.0 were found to be acceptable. As a result of the application of the proposed modeling, acceptable levels of compatibility were achieved compared to the empirical BOF data and targeted steel composition. The paper indicates how fuzzy logic would be effectively used for improved process control of BOF furnace in steel making industry.

Scenario based distributed manufacturing simulation using HLA technologies

Simulating complex and distributed manufacturing systems is not easy using traditional simulation techniques. Manufacturing environment contains several systems that must interoperate and exchange information. A general software architecture is necessary to make manufacturing systems interrelated. This paper presents an overview of distributed manufacturing simulation as well as of information representation in distributed manufacturing simulation using high level architecture (HLA) and its object model template (OMT). The concept is explained with a scenario which is provided to better address the object class structure, interaction class structure, attribute, parameter and data type tables.

How successfully does JIT handle machine breakdowns in an automated manufacturing system

Scheduling a manufacturing system can be one of the most complex tasks in managing an operation. Planning and control systems such as just in time (JIT) can aid scheduling. However, planning and control tools require a fairly stable shopfloor environment to get the best out of them. Many system designs and schedules only consider 100 per cent reliability in machines, and do not take into account random interruptions. In this paper, a simulation model was created to investigate machine and material handling system breakdown problems in a JIT‐driven flexible manufacturing system. Results show that compromises have to be made with JIT control in order to get the best system performance.

Comparison of OR and AI methods in discrete manufacturing using fuzzy logic

Since 1950s the techniques of Operations Research (OR) and Optimization have been utilized to increase the efficiency of the production systems. With the widespread use of computers, it has even become easier to deal with industrial problems. However the complexity of the problems still reveals the difficulty in providing solutions. The use of artificial intelligence (AI) seems to attract the attention of the researcher to overcome to the difficulties. This has already been realized with several successful applications. In this study, the use of AI and OR techniques is compared using fuzzy logic. The progress of manufacturing systems, characteristics of production processes, system managements and system behavior are taken into account. The study is focussed on only discrete manufacturing.

Modelling and simulation of MRP II activities in multi agent systems

This paper is modelled in details, and it describes an integrated MRP II agent system for use in a make-to-order manufacturing environment by demonstrating potential benefits on purchasing and manufacturing orders generated. MRP II activities were modelled in a multi-agent based system; the information exchanges and activities to occur within the system were identified and the system simulation was prepared by applying the Petri net method using the estimated operation times for these activities. Multi-agent systems were preferred for modelling due to the fact that these systems were intelligent software systems and they included discrete manufacturing systems as well as communication and software systems. Also, the Petri net system was preferred in simulation because it was one of the distributed artificial intelligence methods and used in the analysis of the status and information exchange in the software systems. The obtained results will provide information about the possible bottlenecks and interruptions to occur before implemented within a huge and complex system structure.

The database management system for Sakarya automotive suppliers and supply chain

This paper describes a database management system which was the result of a study carried out by Sakarya University and Adapazari Chamber of Commerce and Industry. The aim of the study was to increase the competitiveness of automotive companies in national and global markets by providing financial, economical and technological information regarding product design, supply chain, quality and job satisfaction. The results indicated that it is of vital importance to generate a supply-chain database management system for better selection of suppliers. Due to the huge number of data to be considered in decision-making, computer support seems to be inevitable.

Decision support systems in production planning and control

Production planning and control is facing more and more challenging tasks every day. Products are becoming more complex, manual systems are being replaced with complex machines. The world is undergoing a technology revolution and knowledge systems are becoming more dominant then ever before. It seems that the information age will create sophisticated systems requiring complex decisions based on the knowledge of manufacturing and other aspects of organizations. Moreover, traditional manufacturing environments are converging into knowledge-based manufacturing in knowledgeable societies. This obviously indicates the importance of decision support systems which can be developed in such a way that they can utilise knowledge and handle knowledge sources as effectively as possible. The manufacturing industry of the twenty-first century will be characterised by intensive knowledgebased systems of concurrent engineering based on digitalisation, computer network, artificial intelligence, etc. In the coming years, knowledge, agility, intelligence and rapid response are essential requirements for manufacturing systems to favour high quality products, small batch sizes, customer requirements, and environmental consciousness. Taking this into account, the 4th Intelligent Manufacturing Systems Symposium involved a series of papers discussing the decision support which could be created by intelligent systems. Academia and industrial practitioners came together and exchanged their knowledge and experiences with intelligent support systems. The Symposium covered a wide range of manufacturing topics including designing new products, automated storage and retrieval systems, competitive manufacturing strategies and manufacturing knowledge management. The Symposium was a successful event which yielded a series of valuable research publications and discussions. After the discussions, and recommendations by the session chairmen, several papers were nominated to be published in Production Planning & Control. After an extensive review process the papers published in this issue were selected. The papers present results of the studies from improvement through decision making in design to strategic enterprise resource management, from supplier selection to multi agent based simulation and from multi channel scheduling to web-based product development processes. The Symposium will continue biannually to generate a knowledge exchange atmosphere concerning the emergence of new and current technologies creating value for the manufacturing society.

KB-SCHED: Knowledge-based scheduler for complex and dynamic systems

Scheduling problems are becoming more and more complex everyday. This makes the current rules and algorithms difficult to comply with the requirements. New machines with the capabilities of processing more than one jobs is being developed. Sometimes one job is divided into parts and processed by more than one machine at the same time. These make the current algorithms insufficient. Artificial intelligence technologies, especially expert systems are proven to deal with such dynamic complex problems in several domains. In this study, an example of such a complex problem is introduced and knowledge-based scheduling for these kind of problems is elaborated with a real life industrial example.

Abstract Information Extraction From Consumer's Comments On Internet Media

information extraction from consumers comments on internet media