Model Of Production Process Research Articles

Reducing the Total Product Cost at the Product Design Stage

Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product.

Analysis of the Dual-phase Steel DP500 Stress-strain Characteristics During the Plane Shear Test

Nowadays, the tendency to survey and accelerate technological processes in order to increase produc-tion efficiency on the one hand and to reduce the ecological impact of production on the environ-ment on the other hand is increasingly prevailing. These aspects encourage both the applilcation of mathematical modelling of production processes and utilization of new material types ensuring suffi-cient strength and ductility while reducing the overall weight of the final asembly. This paper focuses on investigation and evaluation of the mechanical properties of the material under plane shear stress conditions. Specifically, it concerns the testing of a dual-phase high-strength steel DP500 (designa-tion according to EN 10346) having structure based on a ferritic-martensitic matrix. The test material is loaded by means of a testing machine with a continuous static load and condition of the plane shear stress is achieved due to the geometry of the test specimen (ASTM B831). In the experimental part of this paper, plane shear tests are performed to determine the basic mechanical properties and stress-strain characteristics of the material with respect to the given loading conditions. These deter-mined parameters and material characteristics can be further used as important input data to improve results of the numerical simulations regarding the aspect of metal forming technologies – basically stamping process.

Environmental impact estimation of ceramic tile industry using modeling with neural networks

The ceramic tiles industry has a significant environmental impact due to consumption of raw materials, energy and environmental emissions. There are numerous activities on global level in accordance to the principles of sustainable development. This paper presents the development and application of mathematical models of manufacturing processes based on static neural networks for prediction and control of environmental impact of ceramic tiles production process. The neural network learning is made based on known input and output values from the production process. The control of the environmental impact is made on the basis of the output values from the process amounts of correct and faulty ceramic tiles. The model for prediction of correct amount of tiles and percentage of waste with an average error of 1.7% is presented in this paper. It could be successfully used to estimate and control the environmental influence. A simple model of production process has been applied in the manufacturing process of ceramic tile factory KIO Keramika d.o.o. Orahovica. It produced ceramic tiles using monofiring process according to EN 14 411 B III group Part L. The company has introduced and certified management systems according to ISO-9001. and ISO 14001

Redesigning the Production Process Using Simulation for Sustainable Development of the Enterprise

In this case study, the medium-sized enterprise is looking for ways to achieve sustainable development. Following the Industry 4.0 trend could get the enterprise closer. The rate of information technology usage is very low in the enterprise. The enterprise’s problem is the long production of orders. The orders had to be rejected many times because the customer’s time limit could not be met. The major cause seems to be two environmentally insufficient and obsolete machines in the blast cleaning operation. These machines are replaced in the production by one new wheel blast machine. However, the production process must be redesigned and the layout changed to use this machine effectively. This redesign is difficult to verify and evaluate in the pilot plant experiment. Simulation is the most suitable tool to do it quickly and relatively easily. The ExtendSim simulation program creates a real production process model and then applies the redesign of this process in the model. The model must be thoroughly verified concerning the real production process to not distort the results. The result of the redesign of the production process is the reduction of the production duration by almost 50% and elimination of two environmentally insufficient and obsolete machines. Improved machine blast cleaning has also significantly reduced the utilization of blast cleaning. Spared capacities can be used for pre-production or otherwise for the profit-making of the enterprise. However, a new bottleneck appeared elsewhere in the production process by removing the previous one. The innovation passed through barriers, and Slovak small and medium enterprises started building sustainable development. The enterprise still must focus on further innovation, and more investment will be needed to achieve the final sustainable development goal.

Simulation of ethylene oxide production from ethylene cholorhydrin

This research has been performed in the Ethylene Oxide production process. It is a flammable and colorless gas at temperatures above 11 °C. It is an important commodity chemical for the production of solvents, antifreeze, textiles, detergents, adhesives, polyurethane foam, and pharmaceuticals. Small amounts of Ethylene Oxide [EO] are used in manufacturing fumigants and sterilants for spices and cosmetics, as well as hospital sterilization for surgical equipment. Modern Ethylene oxide [EO] productions employ either air or oxygen (O2)to oxidize ethylene (C2H4) with a silver catalyst on an alumina oxide carrier[Ag/Al2O3]catalyst packed in a fixed-bed reactor (plug-flow reactor)but the oxygen-base reaction process is more desirable here we used oxygen. Mainly two reactions occur, partial oxidation of ethylene to ethylene oxide and total oxidation of ethylene to carbon dioxide and water. The design models of the process in this research based on a three-part system. They are: the reaction system, absorption system and Ethylene Oxide [EO] purification system. The largest cost in production of ethylene oxide is ethylene therefore, it’s important to optimize the selectivity towards ethylene oxide and thus reduce the consumption of Ethylene. The aim of this work is to create a simulation model of the Ethylene Oxide production process from Ethylene using Aspen Hysys V9. Also to knowing the optimum operational conditions (temperature –pressure –flow rate) for the oxidation reactions of Ethylene. The simulation was running three times with various operational conditions to make a good result. The conclusion was that during operational time the activation energy increased for both reactions which have to be compensated with increasing reactor temperature. At the same time the selectivity for producing Ethylene Oxide decreases, i.e. more carbon dioxide and water are formed. The simulation models yield Ethylene Oxide with purity of 99.2%.

Numerical simulation of combustion, flow, and heat transfer process of an oilfield heating furnace under different production conditions

AbstractHeating furnace is an important energy‐consuming equipment in oilfield. In order to evaluate the operation status of heating furnace more accurately, in this paper, the production process of heating furnace in an oilfield is studied by numerical simulation technology, and the specific reasons affecting its thermal efficiency are deeply explored. First, the numerical simulation model of oilfield heating furnace production process is established, which is to digitize the geometric structure of the heating furnace, study the characteristics of the geometric shape, and generate the appropriate calculation grid. Second, numerical simulation and analysis are carried out for the working process of the heating furnace under rated and nonrated conditions, respectively. The medium flow characteristics, flame shape, temperature distribution, and pipe temperature distribution in the heating furnace under different conditions are systematically discussed. Finally, through the simulation analysis, it is found that the burner rotating device is beneficial to strengthen the heat transfer, and the change of load will affect the efficiency of the heating furnace. Under the rated working condition, design exhaust temperature is 260℃, numerical result is 225.8℃, and the error is −13.2%; design export temperature of water is 60℃, numerical result is 58.1℃, and the error is −3.2%; design temperature difference between inlet and outlet of water is 30℃, numerical result is 28.1℃, and the error is −6.3%. The working process of oilfield heating furnace is a complex coupling heat transfer process of combustion, flow, and heat transfer. The numerical results show that the working process of oilfield heating furnace could be successfully realized by means of numerical simulation. Under variable load conditions, in order to ensure that the export temperature of water is close to design temperature, it is necessary to change the fuel quantity in equal proportion to the water quantity. It is hoped that the reported results provide interesting information among the oilfield heating furnace operators.

Integrated inventory model for imperfect production process having preservation facilities under fuzzy and inflationary environment

Integrated inventory model for imperfect production process having preservation facilities under fuzzy and inflationary environment

Automation of a garment sewing department assessment by smart simulation

The textile-garment industry needs great production flexibility to adapt, in terms of sustainability, to the fickle fashion market. This study proposes a new simulation model of manufacturing and production processes, which can be used as design support, performance evaluation and plant management of highly flexible production systems such as those operating in the fashion sector. The proposed simulation model aims to support industries in increasing their reactive flexibility by adjusting the production set-up. It is a distributed discrete event simulator based on the easy re-composition of resources in the production layout and on the availability of intelligent process management and control modules. The simulator has been applied to optimise the production of children's garments. The development was done together with the production manager of an important European industry; the manager enjoyed the intuitive use of the software and the significance and reliability of the information obtained.

Novel Production Prediction Model of Gasoline Production Processes for Energy Saving and Economic Increasing Based on Am-Gru Integrating the Umap Algorithm

Novel Production Prediction Model of Gasoline Production Processes for Energy Saving and Economic Increasing Based on Am-Gru Integrating the Umap Algorithm

An integrated inventory model for imperfect production process having preservation facilities under fuzzy and inflationary environment

An integrated inventory model for imperfect production process having preservation facilities under fuzzy and inflationary environment

Applications of a “Carry-Over” Enhanced DSBM Intertemporal Technical Efficiency Model on the Taiwanese 5G Industry

Abstract Taiwan has proven itself successful at both inventing the key technologies leading to the development of 5G (fifth generation wireless technology)-related industries and serving as an indispensable link in the burgeoning 5G-industrial global supply chain. This study analyzes the current state of Taiwan’s 5G industry via the utilization the purpose of this study is to the Dynamic Slacks-Based Measure (DSBM). To achieve this purpose, a dynamic-data production process model was developed to analyze the 5G industry’s overall relative efficiency. Results indicate that (1) key chip-producing companies typically experience increased efficiency following 5G R&D industry development investment, and that said companies’ relative efficiency is, indeed, affected positively by R&D investment; and (2) key chip companies’ relative increases of efficiency were higher than those of brand terminal and downstream-industry-category companies, while companies with higher levels of R&D investment exhibited relatively higher and more significant levels of efficiency. Finally, it was discovered that the relative efficiency of Taiwan's 5G-related industries’ R&D investment was, indeed, statistically significant in terms of the Taiwanese government’s industrial policies regarding 5G R&D investment. Keywords: Dynamic Slacks-Based Measure (DSBM), 5G Industry, R&D Inputs.

An Enabling Open-Source Technology for Development and Prototyping of Production Systems by Applying Digital Twinning

This article presents the most valuable and applicable open-source tools and communication technologies that may be employed to create models of production processes by applying the concept of Digital Twins. In recent years, many open-source technologies, including tools and protocols, have been developed to create virtual models of production systems. The authors present the evolution and role of the Digital Twin concept as one of the key technologies for implementing the Industry 4.0 paradigm in automation and control. Based on the presented structured review of valuable open-source software dedicated to various phases and tasks that should be realised while creating the whole Digital Twin system, it was demonstrated that the available solutions cover all aspects. However, the dispersion, specialisation, and lack of integration cause this software to usually not be the first choice to implement DT. Therefore, to successfully create full-fledged models of Digital Twins by proceeding with proposed open-source solutions, it is necessary to make additional efforts due to integration requirements.

The use of simulation methods to analyze the functioning of kefir production processes

The article is devoted to increasing the efficiency of kefir production through the use of the simulation method, which allows you to create a multi-agent simulation model that describes the real processes of the studied production in the virtual space. The advantages of using simulation models in comparison with conducting experiments in a real system and using other methods are shown. Such a model allows you to obtain detailed statistics on various aspects of the functioning of the system under study, depending on the input data, reflecting the processes as if they were happening in reality. The article defines the relevance of the topic. It is shown that in the process of producing kefir there are problems typical for the market: constantly changing consumer demand for products, the emergence of new varieties of products, new technologies and modern progressive equipment. To meet these requirements, a flexible, rapidly restructuring production organization scheme is needed, which will work effectively with the introduction of an intelligent system for automatic quality control of kefir production and will contribute to a quick and effective restructuring of the technological equipment used, as well as technological modes of production. Therefore, the analysis of the structure of the dairy enterprise from the point of view of the main events of the modeled system was carried out, on the basis of which a logical and mathematical model of kefir production in the form of a multiphase multichannel system was developed and presented. Also, a systematic analysis of the automation object - the kefir production line was carried out. All stages of kefir production are considered, taking into account the information processes occurring in them, which made it possible to develop a flow diagram of the relationship between different workshops and raw material flows in the production of kefir. The carried out experimental studies allowed to obtain a multi-agent simulation model of kefir production. To implement the presented model, the software product AnyLogic was used. After the development of this model, 2D and 3D simulation models of the kefir production process were presented to test its operability. This article lists all the agents of the model.

Econometric modeling of agricultural production processes in Uzbekistan

Econometric modeling of agricultural production processes in Uzbekistan

PRESTO: Predictive REcommendation of Surrogate models To approximate and Optimize

Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate. Many techniques exist for surrogate modeling; however, selecting suitable techniques for a given application remains an open challenge. This work describes PRESTO, a Random Forest classifier-based tool, to recommend appropriate surrogate modeling techniques for a given dataset for surface approximation and surrogate-based optimization, using attributes calculated only using the input and output data. The tool identifies the techniques for surface approximation with an accuracy of 91% and a precision of 90% and for surrogate-based optimization with an accuracy of 98% and a precision of 99%. PRESTO was tested on data generated from a high fidelity process model of the cumene production process. Its performance on this case study was comparable to the training data. PRESTO enables computational time savings for selecting surrogate model forms by avoiding expensive trial-and-error methods.

Research of thermal energy processes during silicon carbide production in the resistance furnace

Thegoal. Silicon carbide is one of the most essential artificial inorganic materials widely used to produce abrasive instruments, high-temperature heaters, fireclay ceramics, and metallurgy. The most amount of silicon carbide produces in the resistance furnaces. One of the issues of the silicon carbide production process is the lack of supervisory of thermal state in the furnace working space, which does not always allow for select rational electrical modes and accordingly get the material of appropriate quality. One of the methods to solve this issue is computer modeling the thermal state of the resistance furnace. Therefore, the goal of this research is the development of a computer model of the thermal state in the reaction zone of the resistance furnace, that further allows the development of technological recommendations concerning modes of conducting the process of obtaining silicon carbide. In addition, the development of a mathematical model of the dynamics of the thermal state of the resistance furnace opens up new opportunities for improving the existing automated control systems, which is very important from the point of view of the implementation of the Industry 4.0 paradigm at the enterprises of the mining and metallurgical complex of Ukraine. Methodology. The thermophysical model of the silicon carbide production process in the resistance furnace was developed. By finite difference method, the dynamics of the thermal state of the reaction zone in the furnace are computed. Resultsandscientificnovelty. Zones of the existence of products of carbothermic reduction of silica due to the heat generated when an electric current is passed through the furnace core are sized up by results of modeling, and the temperature front of the progress of the reductive reactions was detected. Practical value. The developed model allows for evaluating the influence of supplied power dynamics on the reduction products size zones, obtaining the analytical dependences of changes in the thermal state of the reaction furnace zone, and thereafter developing the technological recommendations concerning silicon carbide production process performance and improvements for the furnace automated control system. The structure of control of the thermal modes of the resistance furnace and the control computer complex based on Siemens technology is proposed.

Simulation of Lovastatin Production in Solid-State Fermentation via Oil Palm Frond

Lovastatin is a potent drug for lowering the blood cholesterol. It is a competitive inhibitor of 3-hyroxy3-methyl glutaryl coenzyme A (HMG-CoA) reductase, which is a key enzyme in the cholesterol production pathway. Lovastatin increases the good cholesterol or high-density lipoproteins to prevent the formation of plaque inside the blood vessels. This study aims to develop a process model of lovastatin production, produced by Monascus purpureus under solid-state fermentation using oil palm frond. SuperPro Design V9.5 software was used to develop and simulate the process model. Three parameters which are initial moisture content, composition of peptone and potassium, were varied to investigate their effects on lovastatin production. The optimum condition simulated using the process model at pH 7 with 60% initial moisture content, 0.0075 kg/hr of potassium, and 0.0075 kg/hr of peptone was able to produce 0.0288 kg/kg of lovastatin. The simulated results show good agreement with experimental work, with low percentage error of 5.77%, and provide a good approximation on the production of lovastatin under various process operating conditions.

Resilience in supply systems – What the food industry can learn from energy sector

Drastic events such as pandemics, earthquakes or other disasters not only threaten the immediate living conditions of people but also indirect circumstances such as energy supply, infrastructure and food production. To ensure that damage and failures in these areas do not lead to a disaster, special requirements are placed on this critical infrastructure. In this context resilience, which is defined as the resistance of a system to external effects, is required. A field that is indeed part of the critical infrastructure, but which has not been considered as intensively as the energy sector, is food production.The investigation focuses on how fundamental principles of thermodynamics, system theory and reliability theory can be applied to the modelling of food production processes to obtain a measure of resilience. Using known state and process variables from thermodynamics and electrical power engineering, analogous variables are derived for the food industry. These variables serve as an evaluation standard for a quality measure Q. In addition to system-theoretical considerations, it is investigated how existing evaluation criteria of power engineering, such as System Average Interruption Duration Index (SAIDI) and Customer Average Interruption Duration Index (CAIDI), can be transferred to food production.Design: The investigation focuses on how fundamental principles of thermodynamics, system theory and reliability theory can be applied to the modelling of food production processes to obtain a measure of resilience. Using known state and process variables from thermodynamics and electrical power engineering, analogous variables are derived for the food industry.Purpose: Drastic events such as pandemics, earthquakes or other disasters not only threaten the immediate living conditions of people but also indirect circumstances such as energy supply, infrastructure and food production. To ensure that damage and failures in these areas do not lead to a disaster, special requirements are placed on this critical infrastructure. In this context resilience is required.Findings: The aforementioned state and process variables serve as an evaluation standard for a quality measure Q. In addition to system-theoretical considerations, it is investigated how existing evaluation criteria of power engineering, such as SAIDI and CAIDI, can be transferred to food production.Originality: This paper fulfils an identified need to study a part of the critical infrastructure that has not been as extensively looked at as the energy sector, namely food production.

Multi-level production process modeling language

The fourth industrial revolution introduces changes in traditional manufacturing systems and creates basis for a lot-size-one production. The complexity of production processes is significantly increased, alongside the need to enable efficient process simulation, execution, monitoring, real-time decision making and control. The main goal of our research is to define a methodological approach and a software solution in which the Model-Driven (MD) principles and Domain-Specific Modeling Languages (DSMLs) are used to create a framework for the formal description and automatic execution of production processes. In that way production process models are used as central artifacts to manage the production. In this paper, we analyze production process modeling domain and present a DSML which can be used to create production process models suitable for automatic generation of executable code. The generated code is used for automatic execution of production processes within a simulation or a shop floor. The language can be used to specify errors that may occur during the process execution and to specify error handling and corrective steps, too. The DSML is evaluated by different groups of users and the evaluation results are presented. Both the DSML and the accompanying modeling tool are still in the prototype phase, as they are created and evaluated in use cases covering just the assembly of goods. To enable wider application of the language and the tool, it is required to have additional use cases from different manufacturing domains.

Managing a sustainable integrated inventory model for imperfect production process with type one and type two errors

Unlike previous research, this study develops an integrated inventory model for controllable lead time with defective items, errors in inspection, and variable lead time considering sustainability. The research investigates the effect of controlling lead time and capital investment in the setup cost. We assume that the buyer receives a lot size that may contain some defective items with a known defective probability. The buyer’s inspector conducts a 100% quality inspection and may incorrectly classify a non-defective item as a defective item (type one (I) error) or incorrectly classify a defective item as a non-defective item (type two (II) error). The mathematical inventory model considering carbon emission cost is developed, and the solution procedure is designed using the heuristic algorithm to derive the optimal or near optimal solution. Finally, numerical examples and sensitivity analysis are given to illustrate the results. The results show that the defective rate, and type I type II inspection errors, have a significant impact on the shipment lot. This leads to the changes in the total cost, lead time, and the carbon emissions. Our study provides cost savings of 4.39% and carbon emission savings of 28.44%.Graphic abstract