910 resultados para development of manufacturing process


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The purpose of this study is to carry on a thermoeconomic analysis at a biodiesel production plant considering the irreversibilities in each step (part I: biodiesel plant under study and functional thermoeconomic diagram [1]), making it possible to calculate the thermoeconomic cost in US$/kWh and US$/l of the biodiesel production, and the main byproduct generated, glycerin, incorporating the credits for the CO2 that is not emitted into the atmosphere (carbon credits). Assuming a sale price for both the biodiesel and the byproduct (glycerin), the annual revenue of the total investment in a plant with a capacity of 8000 t/year of biodiesel operating at 8000 h/year was calculated. The variables that directly or indirectly influence the final thermoeconomic cost include total annual biodiesel production, hours of operation, manufacturing exergy cost, molar ratio in the transesterification reaction, reaction temperature and pressure in the process. Depending on the increase or decrease in sale prices for both biodiesel and glycerin, the payback is going to significantly increase or decrease. It is evident that, in exergy terms, the sale of glycerin is of vital importance in order to reduce the biodiesel price, getting a shorter payback period for the plant under study. © 2013 Elsevier Ltd. All rights reserved.

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Includes bibliography

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Supply chain starts with a demand arisen and ends with material transport and delivery at its final destination. With this in mind, most of manufacturing, processors or distribution companies of consumer goods, spare parts and components for production, processing and finished goods, within national or international markets, may not have information and control over its supply chain performance. This article presents concept and logistics models evolution, purchase order and international supplier management, control tower and its logistics information systems. This also presents a real process implementation for a global high tech manufacturer company.

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The increasing aversion to technological risks of the society requires the development of inherently safer and environmentally friendlier processes, besides assuring the economic competitiveness of the industrial activities. The different forms of impact (e.g. environmental, economic and societal) are frequently characterized by conflicting reduction strategies and must be holistically taken into account in order to identify the optimal solutions in process design. Though the literature reports an extensive discussion of strategies and specific principles, quantitative assessment tools are required to identify the marginal improvements in alternative design options, to allow the trade-off among contradictory aspects and to prevent the “risk shift”. In the present work a set of integrated quantitative tools for design assessment (i.e. design support system) was developed. The tools were specifically dedicated to the implementation of sustainability and inherent safety in process and plant design activities, with respect to chemical and industrial processes in which substances dangerous for humans and environment are used or stored. The tools were mainly devoted to the application in the stages of “conceptual” and “basic design”, when the project is still open to changes (due to the large number of degrees of freedom) which may comprise of strategies to improve sustainability and inherent safety. The set of developed tools includes different phases of the design activities, all through the lifecycle of a project (inventories, process flow diagrams, preliminary plant lay-out plans). The development of such tools gives a substantial contribution to fill the present gap in the availability of sound supports for implementing safety and sustainability in early phases of process design. The proposed decision support system was based on the development of a set of leading key performance indicators (KPIs), which ensure the assessment of economic, societal and environmental impacts of a process (i.e. sustainability profile). The KPIs were based on impact models (also complex), but are easy and swift in the practical application. Their full evaluation is possible also starting from the limited data available during early process design. Innovative reference criteria were developed to compare and aggregate the KPIs on the basis of the actual sitespecific impact burden and the sustainability policy. Particular attention was devoted to the development of reliable criteria and tools for the assessment of inherent safety in different stages of the project lifecycle. The assessment follows an innovative approach in the analysis of inherent safety, based on both the calculation of the expected consequences of potential accidents and the evaluation of the hazards related to equipment. The methodology overrides several problems present in the previous methods proposed for quantitative inherent safety assessment (use of arbitrary indexes, subjective judgement, build-in assumptions, etc.). A specific procedure was defined for the assessment of the hazards related to the formations of undesired substances in chemical systems undergoing “out of control” conditions. In the assessment of layout plans, “ad hoc” tools were developed to account for the hazard of domino escalations and the safety economics. The effectiveness and value of the tools were demonstrated by the application to a large number of case studies concerning different kinds of design activities (choice of materials, design of the process, of the plant, of the layout) and different types of processes/plants (chemical industry, storage facilities, waste disposal). An experimental survey (analysis of the thermal stability of isomers of nitrobenzaldehyde) provided the input data necessary to demonstrate the method for inherent safety assessment of materials.

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Model based calibration has gained popularity in recent years as a method to optimize increasingly complex engine systems. However virtually all model based techniques are applied to steady state calibration. Transient calibration is by and large an emerging technology. An important piece of any transient calibration process is the ability to constrain the optimizer to treat the problem as a dynamic one and not as a quasi-static process. The optimized air-handling parameters corresponding to any instant of time must be achievable in a transient sense; this in turn depends on the trajectory of the same parameters over previous time instances. In this work dynamic constraint models have been proposed to translate commanded to actually achieved air-handling parameters. These models enable the optimization to be realistic in a transient sense. The air handling system has been treated as a linear second order system with PD control. Parameters for this second order system have been extracted from real transient data. The model has been shown to be the best choice relative to a list of appropriate candidates such as neural networks and first order models. The selected second order model was used in conjunction with transient emission models to predict emissions over the FTP cycle. It has been shown that emission predictions based on air-handing parameters predicted by the dynamic constraint model do not differ significantly from corresponding emissions based on measured air-handling parameters.

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This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

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This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

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Development of novel implants in orthopaedic trauma surgery is based on limited datasets of cadaver trials or artificial bone models. A method has been developed whereby implants can be constructed in an evidence based method founded on a large anatomic database consisting of more than 2.000 datasets of bones extracted from CT scans. The aim of this study was the development and clinical application of an anatomically pre-contoured plate for the treatment of distal fibular fractures based on the anatomical database. 48 Caucasian and Asian bone models (left and right) from the database were used for the preliminary optimization process and validation of the fibula plate. The implant was constructed to fit bilaterally in a lateral position of the fibula. Then a biomechanical comparison of the designed implant to the current gold standard in the treatment of distal fibular fractures (locking 1/3 tubular plate) was conducted. Finally, a clinical surveillance study to evaluate the grade of implant fit achieved was performed. The results showed that with a virtual anatomic database it was possible to design a fibula plate with an optimized fit for a large proportion of the population. Biomechanical testing showed the novel fibula plate to be superior to 1/3 tubular plates in 4-point bending tests. The clinical application showed a very high degree of primary implant fit. Only in a small minority of cases further intra-operative implant bending was necessary. Therefore, the goal to develop an implant for the treatment of distal fibular fractures based on the evidence of a large anatomical database could be attained. Biomechanical testing showed good results regarding the stability and the clinical application confirmed the high grade of anatomical fit.

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Simulation techniques are almost indispensable in the analysis of complex systems. Materials- and related information flow processes in logistics often possess such complexity. Further problem arise as the processes change over time and pose a Big Data problem as well. To cope with these issues adaptive simulations are more and more frequently used. This paper presents a few relevant advanced simulation models and intro-duces a novel model structure, which unifies modelling of geometrical relations and time processes. This way the process structure and their geometric relations can be handled in a well understandable and transparent way. Capabilities and applicability of the model is also presented via a demonstrational example.

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As the success of East Asian countries has shown, labor-intensive industry is recognized to lead economic growth in the early stages of development, utilizing relatively low labor costs. This same growth process has already started in South and South East Asian LDCs since the mid-1990s. However, the manufacturing sector in sub-Saharan Africa has been underdeveloped and manufacturing exports, in particular labour-intensive goods, have stagnated. This paper investigates the international competitiveness of the African manufacturing sector and its determinants through an analytical survey of empirical studies and a comparison with Asian low income countries. Empirical evidences indicate that primary factors of competitiveness, namely productivity, labour cost and exchange rate are unfavorable in sub-Saharan Africa. Representative arguments attribute the weak competitiveness to problems in the business environment, factor endowment, and the exchange rate. However, careful review shows that labour cost is beyond the range explained by endowment and misalignment of exchange rates have been reduced in Africa. Moreover, comparison with Asian low income countries which have competitiveness in labour-intensive goods shows no difference in the quality of business environment, while the labour cost is significantly lower than sub-Saharan African countries. Although results should be considered tentative, high labour cost beyond endowment and conservative investment behavior emerge as important factors for the weak competitiveness in sub-Saharan Africa when controlling income level.

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This paper examines the "catching up" process of manufacturing in East Asia within the framework of North and South location. Results of this study indicate that latecomers of the ASEAN Four and China have advanced the "catching up" process. At the same time, second-runners of the Asian NIES have more extensively increased their "catching up" with Japan. Most "catching up" was realized in a very short period in the 1990s, and the advancement of the "catching up" process has moved into various industries from nondurable products to light machinery products. However, it has not yet advanced in heavy machinery such as in the industrial machinery and machine tool industries.

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Laser Welding (LW) is more often used in manufacturing due to its advantages, such as accurate control, good repeatability, less heat input, opportunities for joining of special materials, high speed, capability to join small dimension parts etc. LW is dedicated to robotized manufacturing, and the fabrication cells are using various level of flexibility, from specialized robots to very flexible setups. This paper features several LW applications using two industrially-scaled manufacturing cells at UPM Laser Centre (CLUPM) of Polytechnical University of Madrid (Universidad Politécnica de Madrid). The one dedicated to Remote Laser Welding (RLW) of thin sheets for automotive and other sectors uses a CO2 laser of 3500 W. The second has a high flexibility, is based on a 6-axis ABB robot and a Nd:YAG laser of 3300 W, and is meant for various laser processing methods, including welding. After a short description of each cell, several LW applications experimented at CLUPM and recently implemented in industry are briefly presented: RLW of automotive coated sheets, LW of high strength automotive sheets, LW vs. laser hybrid welding (LHW) of Double Phase steel thin sheets, and LHW of thin sheets of stainless steel and carbon steel (dissimilar joints). The main technological issues overcame and the critical process parameters are pointed out. Conclusions about achievements and trends are provided.