Model Of Production Process Research Articles

Calculation of permissible monomer conversion in cascade polymerizers during the synthesis of general-purpose rubbers

The generalized model of combined chemical transformation and heat exchange is represented by a set of equations of the thermal balance and initial moments of MWD in stationary mode. A mathematical model of production process of synthesis of diene rubber on cobalt-based catalytic system consisting of kinetic and energy module is developed. The calculation algorithm of mathematical model of technological process of production of rubber SRDC on the basis of which numerical experiments are carried out is offered. The maximum permissible conversions are calculated. The influence of the initiator concentration, monomer and the temperature of the cascade input flow on the temperature of the cascade reactors and the conversion is researched.

Study on Material and Energy Flow in Steel Forging Production Process

Forging production is widely used in aerospace, automobile, shipbuilding and other industries because of its high mechanical properties. Due to the high energy consumption and high pollution in steel and iron enterprises, with the promotion of green manufacturing and energy saving and emission reduction, more and more attention has been paid to the study of energy conservation problem. In this paper, combined with the process of forging production, the material flow of steel, fuel and other related accessories in the production process is studied and analyzed. Based on the material flow model, the energy flow direction of the forging process is qualitatively determined with the material as the carrier, and the energy flow model of the whole forging production process is carried out.

Constructing network enterprise structure to create innovative products

The subject of the study is the formation of the structure of a network enterprise, considered as a set of interacting enterprises in a networked Internet environment that implements a value chain. To build the structure of the network enterprise, it is proposed to use and support the ontology of the network enterprise, which conceptually reflects the models of products and related production and business processes throughout the life cycle. At the same time, the focus is on the implementation of flexible processes for creating innovative products using intelligent model-oriented technologies.The purpose of the study is to build an algorithm for forming the structure of a network enterprise that would ensure the best implementation of the value chain with minimal risks of mismatch of designs and production processes with qualitative value characteristics and requirements for an innovative product. The construction of an algorithm for forming the structure of a network enterprise involves solving the problems of modeling the structure of an innovative product based on an analysis of qualitative value characteristics and requirements for product components, its creation processes, distribution of roles of enterprise participants and analysis of their capabilities.Methods.As the main research method is the method of constructing a model of “digital thread” of creating an innovative product. The most complete application of this method is carried out as part of the reference model of enterprise architecture for Industrie 4.0 (RAMI). The resulting conceptual model of an innovative product and related production and business processes is implemented using an ontological approach. It is proposed to use a combination of QFD (Quality Function Deployment) methods for deploying the structure of a network enterprise and analyzing the types and consequences of potential FMEA (Failure Mode and Effects Analysis) inconsistencies.The main results of the studyare ontology and the algorithm for forming the structure of the network enterprise. A distinctive feature of the proposed ontology of the network enterprise is a clear separation of the valuable qualitative characteristics of the product and the requirements for its creation, as well as the allocation of the abilities of participants in enterprises to implement the necessary processes. The novelty of the presented algorithm for the formation of the structure of a network enterprise lies in the combined application of the QFD and FMEA methods, as well as in the iteration of modeling the structure of an innovative product from the position of the best implementation of quality value characteristics and functional requirements.Conclusions, prospects.The proposed algorithm for creating the structure of a network enterprise allows you to get the best decisions on the criterion for assessing the highest rating for the implementation of quality characteristics and requirements for the components of the value chain and its participants, provided that minimal risk assessments of the mismatch between the designs and processes of creating innovative products are obtained. The developed ontology and the algorithm for forming the structure of the network enterprise is of practical importance for creating an intelligent system for supporting the adoption of innovative decisions for the dynamic construction of network enterprises in the Internet environment.

TWO-FACTOR PRODUCTION FUNCTIONS WITH GIVEN MARGINAL RATE OF SUBSTITUTION

The article is devoted to the study of inverse problems of identifying two-factor production functions from given marginal rate of technical substitution. The analytical forms of twofactor production functions with given linear-fractionalmarginal rate of technical substitution of labor by capital. Classes of two-factor production functions that correspond to given (constant, linear, linear-fractional, exponential, etc.) marginal rate of technical substitution areindicated. The obtained results can be applied in modeling of production processes.

Mathematical modeling of the building materials production processes

We propose a new mathematical model of the process of production of building materials based on the reaction of autocatalysis. Discrete kinetic models can be used to mathematically describe this process with a different number of reagents. In the case of two reagents, the Carleman discrete kinetic system can be used to describe the autocatalysis reaction. The mathematical model proposed in this paper takes into account the terms of a higher order of smallness for a small parameter. The account of these terms allows to describe more correctly process. The numerical research of the proposed mathematical model is carried out. Numerical results are presented. The analysis and comparison of the obtained numerical results with the results of the numerical study of the kinetic system of the Carleman equations is carried out.

Assessment models for the generalized quality criterion for the production processes of thermal power plants

In order to increase the information reliability of the hardware and software technology for industrial and environmental monitoring, we obtained models of the thermal power plants production processes that can give an assessment of a generalized quality criterion for the occurrence of such technological processes as the preparation of fuel, fuel combustion, removal of flue gases and suspended particles. To form the measured values of the parameters of each technological process, a multiversion approach is used, which is based on obtaining the n-th amount of measured data.

Микро-потоки работ: сочетание потоков работ и потоковой обработки данных для поддержки цифровых двойников технологических процессов

Recently, there has been an explosive growth in the development of the concept of Industry. One of the most important elements of this concept is the application of methods of mathematical modeling and intelligent data analysis to create models of production processes and final products based on the processing of signals coming from intelligent sensors. A set of such models, which represent a virtual representation of industrial processes, systems and equipment, is called twin. Digital twins use data from sensors installed on production lines or on the basis of final products to predict equipment failures, optimize product quality and reduce the environmental impact of production processes. The model sets, underlying digital twins, can be described as workflows consisting of a set of computational services, each of which is a model of a process step. To provide flexible support for cloud computing to perform digital twins, we offer the concept of Micro-Workflows, which combines the power of the Scientific Workflows concept, the flexibility of container technology and the sustainability of the Stream Processing approach in distributed computing systems.

Improved Intelligent Identification of Hammerstein-Wiener Systems by Particle Swarm Optimization and K-Means Clustering

This paper considers the robust identification of Hammerstein-Wiener systems in the presence of Gaussian or non-Gaussian noises. An improved intelligent identification scheme is exploited by combining particle swarm optimization (PSO) and K-means clustering. The proposed scheme has strong ability to keep the balance between exploration and exploitation. Its procedure is about “global particle swarm optimization search — K-means clustering — local particle swarm optimization search”. The proposed scheme can identify the parameters of the general Hammerstein-Wiener system with dead zone and saturation characteristics, and obtain a more accurate model for the actual production process. Relative to other improved particle swarm optimization methods, the accuracy of parameter estimation is improved by nearly 53 % at data length L=2000. In particular, the method can better model nonlinear dynamics and facilitate the precise implementation of control in chemical production.

Search for supersymmetry in proton-proton collisions at 13 TeV in final states with jets and missing transverse momentum

Results are reported from a search for supersymmetric particles in the final state with multiple jets and large missing transverse momentum. The search uses a sample of proton-proton collisions at sqrt{s} = 13 TeV collected with the CMS detector in 2016–2018, corresponding to an integrated luminosity of 137 fb−1, representing essentially the full LHC Run 2 data sample. The analysis is performed in a four-dimensional search region defined in terms of the number of jets, the number of tagged bottom quark jets, the scalar sum of jet transverse momenta, and the magnitude of the vector sum of jet transverse momenta. No significant excess in the event yield is observed relative to the expected background contributions from standard model processes. Limits on the pair production of gluinos and squarks are obtained in the framework of simplified models for supersymmetric particle production and decay processes. Assuming the lightest supersymmetric particle to be a neutralino, lower limits on the gluino mass as large as 2000 to 2310 GeV are obtained at 95% confidence level, while lower limits on the squark mass as large as 1190 to 1630 GeV are obtained, depending on the production scenario.

Математична модель виробництва електроенергії енергоблоками АЕС для ви- рішення завдань розвитку електроенергетики України

The object of the research is the production processes implemented at NPP of the SE NNEGC Energoatom. The subject of the research is planned preventive repairs of the equipment of NPP power units. The purpose of the work is to develop a mathematical model of production processes implemented at NPPs of the SE NNEGC Energoatom suitable for predicting the monthly dynamics of the volumes of electricity generation by NPP units in various scenario conditions for the development of the country's nuclear power industry. The model was developed on the basis of a comprehensive analysis of the available data on the installed capacities of power units, the volumes of electricity generation by them, the frequency and duration of previously completed major and intermediate maintenance of the main equipment of NPPs. The model adequately reproduces the general characteristics of the PPM of the equipment of power units, and their impact on the monthly electricity production volumes of each NPP in the medium and long term. The mathematical model is focused on the use as a part of Ukrainian power industry modeling systems and makes it possible to take into account various development strategies of the SE NNEGC Energoatom in such systems, in particular, the commissioning of new power units with regulated characteristics of periodicity and duration of preventive maintenance.Ref. 22, pic. 4, tabl. 2

CFNN-PSO: An Iterative Predictive Model for Generic Parametric Design of Machining Processes

ABSTRACTEvery production process consists of a large number of dependent and independent variables, which substantially influence the quality of the machined parts. Due to the large impact of process variabilities, it is difficult to design optimal models for the machining processes. Mathematical or numerical models for production processes are resource driven, which are not cost effective approaches in terms of computation and economical production. In this paper, a new artificial neural network (ANN) based predictive model is introduced, which exploits particle swarm optimization (PSO) algorithm to minimize the root mean square errors (RMSE) for the network training. This approach can effectively obtain an optimized predictive model that can calculate precise output responses for the production processes. In order to verify the proposed approach, two case studies are considered from literature and shown to produce significant improvements. Furthermore, the proposed model is validated on abrasive water jet machining (AWJM) with industrial garnet abrasives and optimal machining conditions have been obtained with optimized responses, which are substantially improved while compared with gray relational analysis (GRA).

Lean and agile practices to improve the performance of filling process via simulation and data envelopment analysis

This research aims at identifying the appropriate lean and/or agile practice(s) to improve the performance of filling process in a pharmaceutical industry. Initially, the As-Is simulation model of production processes was built and then run to estimate the process’s output measures; averages of input bottle, work in process, waiting time, cycle time, and output bottles. The simulation results showed overall equipment effectiveness (OEE) score for some production processes were smaller than the recommended minimum world-class values. Consequently, agile and/or lean practices were utilized to generate nine To-Be improvement scenarios. Simulation was run to evaluate the process’s output measures and OEE score for each scenario. Finally, the slack based model (SBM) in data envelopment analysis was adopted to determine the best improvement alternative. The SBM results revealed that the lean practice “adding another head to the labeler machine” is the best alternative that may result in anticipated improvement in the OEE by 13.5%. In conclusion, the lean and/or agile practices can result in significant savings in production and quality costs.

Sequential model-based A- and V-optimal design of experiments for building fundamental models of pharmaceutical production processes

Sequential model-based A- and V-optimal experimental designs are known to be effective for maximizing the information content of data, leading to reliable parameter estimates and model predictions. A- and V-optimal designs require inversion of the Fisher Information Matrix (FIM), which may be noninvertible especially for fundamental models with many parameters. In this study, two different methodologies for selecting sequential approximately A- and V-optimal experiments are compared for situations where the FIM is noninvertible. The first approach, called Leave Out (LO) approach, finds and leaves out problematic parameters that make the FIM noninvertible and the second approach, called Pseudoinverse (PI) approach, uses a Moore-Penrose pseudoinverse of the FIM. Comparisons are carried out using a Michaelis Menten reaction dynamic model for production of a pharmaceutical agent. Monte Carlo simulations indicate, for both A- and V-optimal situations, that designed experiments using the LO approach are superior to designs obtained by the PI approach.

МЕЖДИСЦИПЛИНАРНЫЕ УЧЕБНЫЕ МОДУЛИ В МАТЕМАТИЧЕСКОЙ ПОДГОТОВКЕ ИНЖЕНЕРОВ ЛЕСНОЙ ОТРАСЛИ

Problem and purpose. Transformations in the engineering industry, related to equipment upgrading, improvement of technological processes, informatization and mathematization of production, require the training of qualified engineering personnel who are ready to solve production problems in new conditions. Hence the role of fundamental training of graduates, their readiness to implement a complex of knowledge, research activities, modelling of production processes and self-education is increasing. An interdisciplinary approach to education allows us to solve the indicated problems. The purpose of the article is to analyse the experience of preparing students in the format of updated requirements, justifying the need for an interdisciplinary approach to the training of qualified specialists. The development of readiness for professional activity on the basis of mathematical knowledge and distance learning in cross-disciplinary educational modules is described. The methodology of the research consists of the analysis of regulatory documents in the field of higher education, the requirements of educational standards, analysis and synthesis of research works on the subject. The study is based on the theories of contextual, interdisciplinary, competence, activity and student-centered approaches, as well as the fundamentalisation of knowledge. Conclusion. The implementation of a cross-disciplinary educational module activates the cognitive activity of students, participation in research is being developed to solve tasks, readiness for independent activity and self-education, the role of information and communication technologies is increasing.

Multivariate estimation of production duration of steel wire batches on the basis of situational-regulatory models. Message 1

The article considers the tasks of system analysis and development of models required for the synthesis of a situational (multivariate) procedure for estimating the standard duration of manufacturing a batch of products within a multi-structural steel wire complex (object of study), including independently functioning units with continuous, semi-continuous and discrete technological processes (etching, drawing, annealing, copper plating), which are connected by a single material flow. The complex is distinguished by: a variety of technological routes, allowing to produce a wide range of products (steel wire), corresponding to different standards, steel grades, diameters, shapes and masses of finished products; multivariate specialization of drawing mills; flexible connections between departments; parallel, sequential and combined work of the main and auxiliary equipment; equipment by specialized vehicles (cranes, conveyors, transfer carts, electric vehicles). During the system analysis of the research object, the following issues were resolved: a number of technological routes in the branches of complex were identified and described, their characteristics were evaluated. Graphic models of production processes have been developed, displaying sequence and parallelism of operations, their decomposition into elements and microelements for each compartment. The determining factors were identified characterizing the organization of production processes for all departments. Regulatory models have been developed for the duration of operations based on the integration of various research methods. The solution of the above task is based on the clock approach and includes: building a factor model of the piece situational tact of the s -type drawing mill, “pickling bath-tap” subsystem, heat treatment furnace, and the copper plating line. Additionally, the concept of piece equivalent operation of equipment was introduced to bring to a comparable form with the strokes of coarse-drawing mills. To ensure the coordinated work of the coarse drawing department with other departments, an appropriate amount of pickling, thermal, fine drawing equipment and copper plating has been determined. Models of interconnected part-time steps of the work of previous and subsequent branches (in relation to the rough drawing department) are formed. The degree of work coordination was determined on the basis of comparison of the part-time work cycles of equipment and vehicles at the entrance and exit of each section. To do this, regulatory models of vehicle operation were pre-built. The results of the performed work allow us to proceed to the presentation of the algorithm itself for estimating production duration of batches of steel wire, which will be presented in the second message.

Process Integration and Feedstock Optimisation of a Two-Step Biodiesel Production Process from Jatropha Curcas Using Aspen Plus

Abstract Jatropha curcas oil (JCO) has been recognized as a viable non-edible feedstock for biodiesel production with the focus of achieving lesser reliance on fossil fuels. The aim of this work is to integrate and simulate the production of biodiesel from Jatropha curcas oil by a two-step process; a hydrolysis step and a trans-esterification step. The challenge is then to optimise the feedstock ratios to obtain the minimal water and methanol consumption to give optimal biodiesel yield. For this purpose, steady-state simulation model of a two-step production process of biodiesel from Jatropha curcas oil was prepared using ASPEN Plus V8.8. The response surface methodology (RSM) based on a central composite design (CCD) was used to design optimisation experiments for the research work. From the ANOVA, methanol/oil ratio of the trans-esterification step was found to have a significant effect on the biodiesel yield compared to the water/oil ratio of the hydrolysis step. The linear model developed was shown to be a good predictor of feedstock ratios for biodiesel yield. The surface plot revealed that both feedstock ratios do not show a significant combinatorial effect on each other. Numerical optimisation gave the optimum values of the feedstock ratios as a methanol/oil ratio of 2.667 and a water/oil ratio of 1. The optimisation results also indicated a predicted optimum biodiesel yield of 10.0938 kg/hr.

Application of Technological System Based on Processes Modelling and Analysis: A Case Study in Italian Aquaculture Company

Aquaculture is a growing sector and could satisfy the future worldwide fish demand. New sustainable consumption patterns arise from food market, based on the consumer’ need to know more information about food product. Technological and methodological innovations are needed to create more efficiency in aquaculture industry, but also to create product able to respond to the emerging market needs. The paper shows a case study in Aquaculture Company useful to demonstrate how collect product and production processes information and use it for traceability purpose. Starting from the as-is production process analysis and modelling, several strategically information for consumer are identified. The possibility of using the devices developed in S.E.A. research project has been exploited to collect some of this information and a new traceability layer was create in order to collect manually the missing data. At last the new production process was modelled. The benefits coming from the system adoption are multiple, for the final consumer that could see demonstrated the food quality and safety, for the company that could increase own environmental, economical and ethical sustainability.

An Integrated Inventory Model for Imperfect Production Process with Backorder Price Discount under Strict Carbon Cap Policy

An Integrated Inventory Model for Imperfect Production Process with Backorder Price Discount under Strict Carbon Cap Policy

Techno-economic analysis of ash removal in biomass harvested from algal turf scrubbers

Large-scale microalgae cultivation for biodiesel production is expected to be performed utilizing open air growth infrastructure that will inherently introduce ash into the system. High ash biomass represents a significant challenge for the production of biofuel as it increases processing capital and operational costs. This study assesses the economic viability of pretreatment to remove ash from biomass grown with an algal turf scrubber (ATS). An engineering process model of biofuel production was developed based on an ATS growth system followed by an ash removal process and conversion of the biomass to fuels through hydrothermal liquefaction. The model was validated with literature data for the growth and conversion processes and with experimental data for 14 de-ashing processes using water washing or alkaline extraction. The engineering process model was integrated with techno-economic modeling to investigate the impact of ash on biomass and fuel selling prices. Capital costs associated with biofuel conversion doubled as ash content increased from 0% to 70%, increasing fuel selling price by 21%. Integrating ash removal resulted in reduced conversion capital costs (14–42%) based on the reduction of total mass processed. However, only water washes scenarios at 25 °C and 50 °C were found to reduce overall fuel selling price, with alkaline extraction scenarios showing a significant increase. Operational expenses associated with alkaline extraction de-ashing including wastewater treatment, chemical costs, and heating the microalgae slurry were found to significantly increase the overall fuel selling price of the microalgae biofuel by 17–92% depending on the operational scenario.

Tools to create innovation investment project based on conversion operation cycle in the context of digitalization

The relevance of the study is determined by the necessity to elaborate the math tools to design investment, innovation and combined projects covering technological, product, enhancing and breakthrough innovations in production technological systems of machine builders. The innovation activity of machine building enterprise should be implemented in the mode ‘think-do-think’ so in digital economy each mastered innovation shall be placed in a digital information analytical system of enterprise managerial accounting. The purpose of the study is to develop the tools to regulate the sequence and substantiation of taking managerial decisions on forming of innovation investment project based on conversion operation cycle in the context of digitalization. The model of production process will be a closed operational cycle of conversion of production capital of enterprise production and technological system to monetary capital in the form of produced and sold products with market added value. The outcome of the study is the developed graphic analytical tools in the form of cashflow vectors graph. Further investigations will be devoted to development of entropic analysis method and design of innovation projects in machine building enterprises.