Optimization of the cyclone separator performance using taguchi method and multi-response pcr-topsis

Pollutant control uses cyclone separators as pre-cleaners and is widely used in manufacturing and mining industries. Research on cyclone performance is carried out with changes in various variations that affect it, the problem that occurs is that multi-response can give results of different factors and levels as a result of equipment design cannot provide optimal results and research topics on inlet scroll types have not been widely carried out, this study aims to improve cyclone performance inlet scroll type separator with helical angle, experimental and development methods to get optimal performance where pressure drop and efficiency are indications of cyclone separator performance, to get optimal performance the use of Taguchi experimental design produces different factors and levels so that multi-response methods such as PCR and TOPSIS was used to produce the best combination of factors and levels, confirmation experiments and computational fluid dynamics (CFD) methods were carried out to ensure the validity of the study, the results showed that the scroll inlet prototype cyclone separator with a helical angle of 150, inlet velocity of 10m/s, outlet diameter of 72 mm provides empirical values ​​for pressure drop and the best particle separation efficiency for multi-parameter responses, further research can be done by modifying the shape and dimensions of the bottom outlet

Simulation Modeling and Analysis for Productivity Improvement in the Production Line

Lean manufacturing addresses the growing need for all types of organizations that drive process change and performance improvements in their organization environment and supports the evolution toward demand-driven supply networks. Lean principles are derived from the Japanese manufacturing industry. It is the set of "tools" that give contribution in the identification and steady elimination of waste (muda). As waste is eliminated, quality improves while production time and cost are reduced. The key to lean manufacturing is to compress time by eliminating waste and this continually improving the process. Ohno (1988) defines waste as all elements of production that only increase cost without adding value that customer is willing to produce. The total productive maintenance (TPM) is mostly regarded as an integral part of Lean. TPM originated in Japan in 1971 as a method for improved machine availability through better utilization of maintenance and production resources. TPM uses an overall equipment effectiveness (OEE) index to indicate equipment and plant effectiveness. The technique works to eliminate the six big losses indicated by Nakajima, as down time (caused by equipment failure, set-up and adjustment), speed losses (owed by idling, minor stoppage and reduced speed) and defects (caused by process defects and reduced yield). The Japan Institute of Plant Maintenance promoted TPM which includes the OEE in 1971. In 1988, Nakajima introduced the TPM to the U.S. OEE has since gained a lot of attention as the ultimate performance measure of a piece of equipment. Sohal et al., (2010), from survey results, found that OEE typically advances from a base measure for efficiency (as its initial purpose), to being a tool to improve effectiveness for analyzing data to support continuous improvement objectives. It’s through the identification and elimination of six big losses, namely (i) breakdowns, (ii) setups and changeovers, (iii) running at reduced speeds, (iv) minor stops and idling, (v) quality defects, scraps, yields, reworks, and (vi) start-up losses. The first two affect Availability rate (A), the second two affect Performance efficiency (P), and the last two affect Quality rate (Q). These three OEE elements, since being introduced by Nakajima until this research was conducted, already experienced several improvements involving a weight calculation method for OEE elements. This study proposes a procedure to obtain weight settings of each OEE element and OEE estimation for productivity improvement in the production line. The first research proposal is sought to offer a procedure to cover the drawbacks of weighting OEE elements. The research motivation was initiated by several researches of OEE improvement, which met difficulty when determining the proper weight for each OEE element. The calculation results of OWEE and PEE by STP also showed better results than the original OEE for the simulation model case study. From the result analysis, it can be concluded that the outcome of this research experiment can be implemented in OEE with a weighted method, among others; for example, in PEE (Production Equipment Effectiveness) as well as OWEE (Overall Weight Equipment Effectiveness). A simulation model was chosen because it is able to mimic a real production line and therefore act as a suitable experiment tool. This study provide a lean overview followed by a description of how simulation is being used to enhance lean performance. This study offering simulation as the lean way to implement and accelerate the TPM. The STP (Simulation Taguchi method Procedure) provided characteristic mapping of OEE elements through a response table. Naturally, even though STP seems to be difficult to implement, the outcome is worthwhile. Moreover, the company will have obvious data to consider when making decisions for the improvement of priorities in their production line. The second research proposal offers OEE enhancement scheme, which provides a company with the appropriate information for decision-making on priority improvement in the production line. By using the Taguchi method and simulation as an experimental tool, this scheme can measure and estimate the contribution for each OEE element to an OEE score. This procedure can be implemented in a specific WS or in a production line if the factory is made up of more than one manufacturing line. They provide measurements for each OEE element in order to observe the extent of the influence the simulation experiment has on the OEE elements and scores. All of those research proposals are to improve the OEE as a KPI in the factory. In order to meet the objective of the TPM itself, increasing the sustainability of the company by continuous improvements

Throughput and Yield Improvement for a Continuous Discrete-Product Manufacturing System

A seam-welded steel pipe manufacturing process has mainly four distinct major design and/or operational problems dealing with buffer inventory, cutting tools, pipe sizing and inspection-rework facility. The general objective of this research is to optimally solve these four important problems to improve the throughput and yield of the system at a minimum cost. The first problem of this research finds the optimal buffer capacity of steel strip coils to minimize the maintenance and downtime related costs. The total cost function for this coil feeding system is formulated as a constrained non-linear programming (NLP) problem which is solved with a search algorithm. The second problem aims at finding the optimal tool magazine reload timing, magazine size and the order quantity for the cutting tools. This tool magazine system is formulated as a mixed-integer NLP problem which is solved for minimizing the total cost. The third problem deals with different type of manufacturing defects. The profit function of this problem forms a binary integer NLP problem which involves multiple integrals with several exponential and discrete functions. An exhaustive search method is employed to find the optimum strategy for dealing with the defects and pipe sizing. The fourth problem pertains to the number of servers and floor space allocations for the off-line inspection-rework facility. The total cost function forms an integer NLP structure, which is minimized with a customized search algorithm. In order to judge the impact of the above-mentioned problems, an overall equipment effectiveness (OEE) measure, coined as monetary loss based regression (MLBR) method, is also developed as the fifth problem to assess the performance of the entire manufacturing system. Finally, a numerical simulation of the entire process is conducted to illustrate the applications of the optimum parameters setting and to evaluate the overall effectiveness of the simulated system. The successful improvement of the simulated system supports this research to be implemented in a real manufacturing setup. Different pathways shown here for improving the throughput and yield of industrial systems reflect not only to the improvement of methodologies and techniques but also to the advancement of new technology and national economy

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1992, volume 1

The 1992 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston and JSC. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, Washington, DC. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers. This document is a compilation of the final reports 1 through 12

A Simulation Model of the “Bio-depot” Concept in the Context of Components of Variance and the “Taguchi Loss Function”

The research focuses on the simulation, statistical evaluation, costs, and continuous improvement of supply chains for bio-based materials. A significant challenge of using cellulosic feedstocks for biofuel or bioenergy production is the high per unit costs of final products, e.g., biofuels. The goal of the research is to provide practitioners with useful statistical methods and a simulation Excel template for evaluating the variance and costs associated with the supply chain of bio-based products. Statistical Process Control (SPC), components of variance, Taguchi Loss Function, and reliability block diagrams (RBD) are used in this thesis for the evaluation of the supply chain system of handling the feedstock components for biofuel production. These statistical methods are well accepted and suitable to assess and monitor the components of the supply chain for biofuel feedstocks, e.g., Switchgrass (Panicum virigatum L.), loblolly pine (Pinus taeda L.) chips, etc. Applying these statistical methods will allow for the quantification of the variance of the system and its components, e.g., feedstock particle size processing, drying, and ash content. The overall goal of the study is to quantify the variation of the components within the supply chains, estimate components costs (and total cost) using the Taguchi Loss Function, and provide suggestions for improvement of the system (www.spc4lean.com)

Low carbon manufacturing: Fundamentals, methodology and application case studies

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The requirement and awareness of the carbon emissions reduction in several scales and application of sustainable manufacturing have been now critically reviewed as important manufacturing trends in the 21st century. The key requirements for carbon emissions reduction in this context are energy efficiency, resource utilization, waste minimization and even the reduction of total carbon footprint. The recent approaches tend to only analyse and evaluate carbon emission contents of interested engineering systems. However, a systematic approach based on strategic decision making has not been officially defined with no standards or guidelines further formulated yet. The above requirements demand a fundamentally new approach to future applications of sustainable low carbon manufacturing. Energy and resource efficiencies and effectiveness based low carbon manufacturing (EREEbased LCM) is thus proposed in this research. The proposed EREE-based LCM is able to provide the systematic approach for integrating three key elements (energy efficiency, resource utilization and waste minimization) and taking account of them comprehensively in a scientific manner. The proposed approach demonstrates the solution for reducing carbon emissions in manufacturing systems at both the machine and shop floor levels. An integrated framework has been developed to demonstrate the feasible approach to achieve effective EREE-based LCM at different manufacturing levels including machine, shop floor, enterprise and supply chains. The framework is established in the matrix form with appropriate tools and methodologies related to the three keys elements at each manufacturing level. The theoretical model for EREE-based LCM is also presented, which consists of three essential elements including carbon dioxide emissions evaluation, an optimization method and waste reduction methodology. The preliminary experiment and simulations are carried out to evaluate the proposed concept. The modelling of EREE-based LCM has been developed for both the machine and shop floor levels. At the machine level, the modelling consists of the simulation of energy consumption due to the effect of machining set-up, the optimization model and waste minimization related to the optimized machining set-up. The simulation is established using sugeno type fuzzy logic. The learning method uses on experimental data (cutting trials) while the optimization model is created using mamdani type fuzzy logic with grey relational grade technique. At the shop floor level, the modelling is designed dependent on the cooperation with machine level modelling. The determination of the work assignment including machining set-up depends on fuzzy integer linear programming for several objectives with the evaluation of energy consumption data from machine level modelling. The simulation method is applied as the part of shop floor level modelling in order to maximize resource utilization and minimize undesired waste. The output from the shop floor level modelling is machine production a planning with preventive plan that can minimize the total carbon footprint. The axiomatic design theory has been applied to generate the comprehensive conceptual model E-R-W-C (energy, resource, waste and carbon footprint) of EREE-based LCM as a generic perspective of the systematic modelling. The implementation of EREE-based LCM on both the machine and shop floor levels are demonstrated using MATLAB toolbox and ProModel based simulation. The proposed concept, framework and modelling have been further evaluated and validated through case studies and experimental results.This work is financially supported by The Royal Thai Government

Study of power quality assessment for a photo-voltaic based Distribution Static Compensator (DSTATCOM)

This thesis presents an idea of PV cell or battery based dc to dc boost converter which is served to a voltage source converter (VSC) for enhancement of quality of power. A synchronous reference controller is suggested using distribution static compensator (DSTATCOM) in a 3-phase 4-wire transmission system. The DSTATCOM mainly comprises of one VSC and a dc-link capacitor. The main purpose of DSTATCOM is to provide source and total harmonic reduction, reactive power compensation and compensation of neutral current at point of common coupling .A PCC is a point on electricity network where consumer loads are connected. The boost converter used does the work of a chopper. It converts variable output dc to a fixed value of dc by stepping up of voltage equal to the DC-link necessity of voltage source converter. The main benefit of this planned scheme is that, it will always deliver continual compensation for the whole day. To provide separation to voltage source converter and path for fundamental of zero sequence components, one star/delta transformer is employed. It also helps to diminish neutral current by supplying a circulating route in secondary winding of transformer which is delta connected. The required gate pulse to IGBT’s and diode are provided from the PWM controller by using synchronous reference algorithm

Multi-criteria decision making approach for vendor selection

In the present study an efficient Multi-Criteria Decision Making (MCDM) approach has been proposed for quality evaluation and performance appraisal in vendor selection. Vendor selection is a Multi-Criteria Decision Making (MCDM) problem influenced by multiple performance criteria/attributes. These criteria attributes may be both qualitative as well as quantitative. Qualitative criteria estimates are generally based on previous experience and expert opinion on a suitable conversion scale (Likert Scale). This conversion is based on human judgment;therefore, predicted result may not be accurate always because the method doesn’t explore real data. These are analyzed using AHP, QFD, Fuzzy techniques etc. reported in literature. In solution of MCDM problems there should be a common trend is to convert quantitative criteria values into an equivalent single performance index called Multi-attribute Performance Index (MPI). Benchmarking and selection of the best alternative can be made in accordance with the MPI values of all the alternatives. In this context, present study highlights application of VIKOR method adapted from MCDM for utilizing quantitative real performance estimate scores. Detail methodology of VIKOR method has been illustrated in this reporting through a case study