Model Of Production System Research Articles

Modular data model for energy-flexible cyber-physical production systems

Due to the massive expansion of renewable energies and the growing importance of carbon pricing, energy prices are rising, and industrial companies must put higher effort in purchasing electrical energy cost-efficiently. Additionally, the increasing share of renewable energies leads to more volatility in the power market, giving companies the opportunity to reduce their energy costs by using energy-flexibility measures. As thermal supply systems offer a great potential for energy flexibility, building automation systems which control the supply systems must be adapted for communication and external access. Intelligent algorithms interacting with the automation can enable factories to fexibilize their energy purchases and to participate in energy markets by using external information, such as energy prices or forecasts of the machine’s energy demand. This requires seamless communication that connects all hierarchy levels of the Reference Architectural Model Industry 4.0 (RAMI 4.0) from connected world to field level. Also functional safety must be ensured. Moreover, energy-flexibility measures must be validated within simulation environments which should show the same data structure. Therefore, a standardized data model that can be applied across different companies is necessary. We propose a concept for a modular and consistent data model for automation, simulation, and communication to enable simple data exchange using external algorithms. Therefore, base classes containing variables for generic actors and sensors are developed and extended by further variables to model systems based on their control mode. Moreover, a safety module to manage external access by algorithms is included. With this data model, industrial energy supply systems can be easily expanded to include additional components. Furthermore, energy supply systems consisting of several components, such as energy converters, pumps and valves can be modeled in an simple and time-efficient way. The data model is applied to a compression chiller at ETA Research Factory.

Process Mining for Dynamic Modeling of Smart Manufacturing Systems: Data Requirements

Modern manufacturing systems can benefit from the use of digital tools to support both short- and long-term decisions. Meanwhile, such systems reached a high level of complexity and are frequently subject to modifications that can quickly make the digital tools obsolete. In this context, the ability to dynamically generate models of production systems is essential to guarantee their exploitation on the shop-floors as decision-support systems. The literature offers approaches for generating digital models based on real-time data streams. These models can represent a system more precisely at any point in time, as they are continuously updated based on the data. However, most approaches consider only isolated aspects of systems (e.g., reliability models) and focus on a specific modeling purpose (e.g., material flow identification). The research challenge is therefore to develop a novel framework that systematically enables the combination of models extracted through different process mining algorithms. To tackle this challenge, it is critical to define the requirements that enable the emergence of automated modeling and simulation tasks. In this paper, we therefore derive and define data requirements for the models that need to be extracted. We include aspects such as the structure of the manufacturing system and the behavior of its machines. The paper aims at guiding practitioners in designing coherent data structures to enable the coupling of model generation techniques within the digital support system of manufacturing companies.

CYBER-PHYSICAL SYSTEMS TECHNOLOGIES AS A KEY FACTOR IN THE PROCESS OF INDUSTRY 4.0 AND SMART MANUFACTURING DEVELOPMENT

The continuous development of production processes is currently observed in the fourth industrial revolution, where the key place is the digital transformation of production is known as Industry 4.0. The main technologies in the context of Industry 4.0 consist Cyber-Physical Systems (CPS) and Internet of Things (IoT), which create the capabilities needed for smart factories. Implementation of CPS solutions result in new possibilities creation – mainly in areas such as remote diagnosis, remote services, remote control, condition monitoring, etc. In this paper, authors indicated the importance of Cyber-Physical Systems in the process of the Industry 4.0 and the Smart Manufacturing development. Firstly, the basic information about Cyber-Physical Production Systems were outlined. Then, the alternative definitions and different authors view of the problem were discussed. Secondly, the conceptual model of Cybernetic Physical Production System was presented. Moreover, the case study of proposed solution implementation in the real manufacturing process was presented. The key stage of the verification concerned the obtained data analysis and results discussion.

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.

Multivariate Statistical Modeling of Crude Oil Viscosity and Mole Percent Components for Reservoir Fluids

This study presents multivariate statistical modeling of PVT properties in a Hydrocarbon Reservoir. Traditionally, these properties are modeled by correlating them with the changes in pressure and temperature associated with them. However, these changes are often a direct corollary of the changing composition as each component’s presence and/or absence contributes to the overall property of the fluid system. In this work, Multiple Linear and Multiple Nonlinear regression models were used to develop a correlation for the crude oil viscosity and the mole percent of the components as they change with pressure and temperature. A fluid system was modelled using the PVT Package of Integrated Production System Modelling (IPM) Suite. The model was then used to perform a differential liberation test simulation to predict the changes in composition of the crude oil. The composition included three lumped crude oil fractions. After the PVT modelling, the generated PVT Composition was used to perform the Multivariate statistical analysis (MVA) and modelling.Two MVA techniques were compared – the Multiple Linear Regression (MLR) and Multiple Non-Linear Regression (MLNR).Analysis of residuals generated from the prediction run based on both techniques showed that the multiple linear regression method had trending residuals, contrary to the law of parametric estimation upon which it is based. However, the multiple nonlinear regression yielded 100% correlation with adequate residual trend and is thus recommended for adaptation to distinct fluid systems. It is imperative to note that the accuracy of this adaptive PVT modeling and prediction is hinged primarily on the accuracy of the PVT model used to estimate the compositional variation.

A comparative study of different pull control strategies in multi-product manufacturing systems using discrete event simulation

Pull production control strategies coordinate manufacturing operations based on actual demand. Up to now, relevant publications mostly examine manufacturing systems that produce a single type of a product. In this research, we examine the CONWIP, Base Stock, and CONWIP/Kanban Hybrid pull strategies in multi-product manufacturing systems. In a multi-product manufacturing system, several types of products are manufactured by utilizing the same resources. We develop queueing network models of multi-stage, multi-product manufacturing systems operating under the three aforementioned pull control strategies. Simulation models of the alternative production systems are implemented using an open-source software. A comparative evaluation of CONWIP, Base Stock and CONWIP/Kanban Hybrid in multi-product manufacturing is carried out in a series of simulation experiments with varying demand arrival rates, setup times and control parameters. The control strategies are compared based on average wait time of backordered demand, average finished products inventories, and average length of backorders queues. The Base Stock strategy excels when the manufacturing system is subjected to high demand arrival rates. The CONWIP strategy produced consistently the highest level of finished goods inventories. The CONWIP/Kanban Hybrid strategy is significantly affected by the workload that is imposed on the system.

Methodology for data assimilation in reservoir and production system to improve short- and medium-term forecast

Numerical reservoir simulation is an important tool that is widely used in the petroleum industry for production forecast and decision-making processes. In a context of integrated production system involving the reservoir, wells, and production facilities, the quality of production forecast depends not only on the quality of reservoir models and well productivity/injectivity but also on the boundary condition for the future extrapolation which, in turn, depends on the production system characteristics. Both systems, reservoir and production facilities, are characterized by uncertain properties and behavior, such as porosity and permeability, among others, for reservoir; pipeline roughness, multiphase flow behavior in pipes, among others, for the production system. This paper proposes a methodology for dynamic data assimilation to reduce uncertainty in both reservoir and production system models to improve short- and medium-term production forecast. The methodology was applied to a benchmark case built using real data from an offshore field in The Campos Basin (Brazil). The results from this study show that it is important to perform data assimilation process in both reservoir and production systems to obtain more reliable production forecasts. We also show that, by focusing on the short- and medium-term production forecast, the production system and reservoir uncertainties cause variability in the production forecast in the same order or magnitude. Based on that, it is recommended to consider multiple solutions of the production system to obtain more realistic production forecasts.

Software Product System Model: A Customer-Value Oriented, Adaptable, DevOps-Based Product Model.

DevOps pipelines have brought notable advantages, such as fast and frequent software delivery to software production paradigms, but dynamically dealing with quality attributes desired by the customer employing a DevOps pipeline remains a challenge. This work aims to define the design of a systems thinking inspired model, called Software Product System Model (SPSM), applying a customer-value oriented, holistic approach for implementing quality requirements, and its application and evaluation in a large software house. The main features include dynamic control of quality gates, the parameters of which are driven by customer requirements and feedback from surveys. All of the inputs are collected in a product backlog and fed forward to the quality gates over the DevOps pipeline. SPSM was successfully deployed in a large software house extending a DevOps pipeline with an accompanying improvement of customer-value oriented key performance indicators for projects. In a 2-year-long case study, security and code quality were the main quality attributes, with the metrics on security vulnerabilities and unit test coverage. At the end of the 2020, the DevOps pipeline within SPSM provided a 69.50% decrease of security vulnerabilities of all software products, and a 29.43% increase in unit test coverage for the whole code base for increasing code quality. At the end of 2020, the project completion ratio was measured to be 99.50% and the Schedule Performance Index (SPI) was measured to be 99.78% as the average of 762 projects delivered. The flexibility of SPSM allowed the software house to adapt to changing customer expectations. A checklist is provided for the replicability of the model application.

Energy balance and efficiency in crop rotation systems

The production system model that has been adopted in most Brazilian regions, the soybean/second corn crop succession, has led to problems in water and soil conservation and favorable conditions for diseases, pests, and weeds in the agricultural system and, consequently, increasing its energy use. Crop rotation is an alternative to this production model, directly interfering with the problematic aspects of the crop succession system and, consequently, its energy balance. Therefore, this study aimed to identify the crop rotation system with the best energy balance and efficiency. The data on the quantity of inputs (seeds, fertilizers, pesticides, and fuel), labor, and grain yield used in the study were collected from a crop rotation experiment conducted at the experimental station of the Rural Development Institute of Paraná - IAPAR-EMATER, Londrina, PR, Brazil, from 2014 to 2020. The experimental design consisted of randomized blocks, with six treatments and four replications. The treatments consisted of T1 (second corn crop/soybean), T2 (white oat/soybean, triticale/corn, and wheat/soybean), T3 (rye + black oat/soybean, black oat + fodder radish/corn, and congo grass/soybean), T4 (canola/corn, crambe/corn; canola/soybean); T5 (buckwheat-fodder radish/corn, bean/soybean, and buckwheat-white oat/ soybean), and T6 (wheat/corn, canola/corn + congo grass, and bean/soybean). The different crop rotations and the traditional second corn crop/soybean system provided positive energy balance and efficiency, that is, they produced more energy than they consumed. The canola/corn, crambe/corn, and canola/soybean rotation systems had the highest energy balance and efficiency, with values of 866,442.27 MJ ha−1 and 10.27, respectively, mainly due to corn cultivation in the summer, which resulted in a higher energy return than the other grain-producing crops.

348 Integration of Nutrition with Air and Water Environmental Concerns

Abstract The dairy and beef cattle industries face a number of environmental challenges related to air and water quality as well as the use of limited resources such as water and fossil energy. Mitigation strategies are available and being developed to reduce environmental impacts, but economical solutions remain a challenge. Assessment of mitigation strategies requires a comprehensive evaluation to assure that benefits in one component are not offset by harm in another. Process-based modeling and life cycle assessment provide tools for conducting this type of integrated evaluation. The most cost-effective strategies often begin with animal feeding. The diet of the animal affects resource use and nutrient excretion where the amount and form of nitrogen and phosphorus excreted affect air and water emissions. National assessments of the U.S. beef and dairy industries indicate where mitigation is most needed. Although greenhouse gas emissions receive most of the concern today, water consumption is another important concern, particularly for producers in drier regions such as the western United States. Over 90% of the water consumed in beef and dairy production is used in irrigated feed-crop production. Therefore, animal diets that use byproduct or other feeds not related to irrigated production can provide large reductions in the water footprint of beef and dairy products. Another emerging concern is that of ammonia emission where beef and dairy cattle may contribute more than half of the national emission inventory. Efficient protein feeding is the most economical and effective step that can be taken to reduce this environmental impact. Simulation of mitigation strategies using production system models provides comprehensive evaluation and prioritization among available and possible options, giving direction toward more sustainable ruminant animal production systems.

Investigation of satellite-related precipitation products for modeling of rainfed wheat production systems

Precipitation is a very important weather variable for growth and yield of rainfed crops. In many agricultural regions of the world, high-quality precipitation records are not available, and thus, gridded precipitation products (GPPs) have to be applied as an alternative. The main objective of this study was to identify the most accurate GPP for simulating crop yield over a major rainfed wheat production zone in Iran. For this purpose, fifteen global GPPs were evaluated versus the observed precipitation records for the simulation of rainfed wheat growth and development and yield estimation using the Cropping System Model (CSM) CERES-Wheat model embedded in the Decision Support System for Agrotechnology Transfer (DSSAT). The findings showed that multi-source GPPs had generally higher skill for the yield estimation. Considering all statistical and simulation results obtained from three sites during 2000–2010, MSWEP (Multi-Source Weighted-Ensemble Precipitation) was found as the best alternative GPP to the observed precipitation data for rainfed wheat grain yield simulation with normalized root mean square error (NRMSE) of 4.6 and Nash–Sutcliffe efficiency (NSE) of 0.79, while CMORPH (the Climate Prediction Center morphing method) was the weakest with NRMSE of 13.3 and NSE as − 0.81. The results point to differences among GPP, but there is a need to evaluate in other regions if multi-purpose GPPs are in general more reliable than GPPs based on specific sources.

Sustainable potential analysis of Indian mackerel (rastrelliger kanagurta) resource in East Coast waters of Aceh Province

Indian mackerel (Rastrelliger kanagurta) was among the most commonly caught fish in Aceh east coast waters. Sustainability of Indian mackerel (R. kanagurta) must be maintained because of its important role in improving the fisheries economy in Aceh Province. This research was conducted in east coast waters of Aceh Province from January 2019-to April 2019. Purposes of this research were to describe the general condition and to find out the sustainable management scenario of the Indian mackerel (R. kanagurta) fisheries in Aceh east coast waters Province. The method used was quantitative descriptive. Data collection was done by conducting interview and literature studies. The material used was on Aceh Province capture fisheries statistical data (2003–2017) obtained from Marine and Fisheries Office Aceh Province. Data were analyzed by applying surplus production and dynamic system modelling. Results indicated that utilization rate value (URy) of the fish stock was 69%. Hence the utilization status of the Indian mackerel (R. kanagurta) resource was moderate exploited. Based on biomass in 2017, the sustainable management scenario of the fish for the next eight years (2018-2025) would achieve the highest allocation of fishing effort equal to total allowable catch (TAC) as big as 163%. Therefore, implementation of fishing efforts equivalent to the TAC regarding the biomass sustainability can be maintained until year 2025.

Constructing a model of national production system for building a circular economy for international trade involvement

The paradigm of unlimited growth capitalism leads to an aggravating the problem of a natural resource shortage, an increase in waste and general pollution of the Earth. The concept of a circular economy (CE) is an alternative to this; it implies a transition to closed production and consumption cycles. The purpose of the article is to supplement this concept with ideas of integrating several rationalizing production models of the CE building at the national production system level, taking into account the country's participation in international trade. These model production models are “flexible custom manufacturing”, “distributed manufacturing” and “lean manufacturing”, which also means the widespread involvement of small and medium-sized enterprises. The use of digital technologies for a new quality of communication, as well as the creation of sharing centers, in order to achieve greater organizational and technological complexity of the production system is required. The CE building must take into account the country's participation in international trade. Attention is focused on the fact that the CE will have a different effect on certain types of international trade, in particular, it will stimulate such trade as: materials for processing, secondary raw materials, technologies, projects of finished products, RD digital platforms for interaction between producers and consumers; industrial parks, clusters and incubators for new industries, as well as technological, digital and organizational innovation stimulation.

The Bernoulli splitting line: analytical and semi-analytical evaluation of the steady-state performance

Many industrial facilities involve splitting production systems of different complexities and arrangements. The accurate and efficient evaluation of the associated performance measures is, therefore, of great importance. This challenging task can be accomplished by applying mathematical models of production system engineering at different intricacies. This paper presents the development and application of the analytical approach to splitting production systems modelling. The same problem is also addressed using the finite state method as a semi-analytical model. Several theoretical examples were considered to validate the finite state method that proved to be quite efficient and accurate. Finally, the developed modelling approach was applied in the case of a wood-processing facility. The obtained results were validated successfully against the acquired factory-floor data.

Case Study of Building a Digital Twin for a Production System

Abstract As the term of Industry 4.0 becomes more and more relevant with each passing day, it is up to researchers and companies to find solutions to integrating all the technologies it covers. One of those technologies, even though not highly developed, is simulation and building Cyber-Physical Systems for gathering data and improving the production processes. In the research described in this paper, we focused on integrating production data with simulation models in order to make the process of understanding and learning about complex production systems as simple and as quick as possible. This paper contains three sections. The first one introduces the theoretical fundamentals of our research. The second one focuses on the methods used to create a digital model of production system. The final one discusses the results of the conducted experiments, and their impact on further research.

Data-driven modelling, analysis and improvement of multistage production systems with predictive maintenance and product quality

Predictive maintenance (PM) and quality management help to improve the business bottom line by alleviating the system performance degradation caused by unscheduled machine breakdown and product quality problems. In modern production systems, the wide application of new IT technology results in data-rich environments. However, it is not clear how to take advantage of the data to facilitate maintenance decision-making and production performance improvement. Aiming at multistage production systems with batching machines and finite buffers, this research studies data-driven modelling, analysis and improvement of production systems with predictive maintenance and product quality. First, a data-driven quantitative method is proposed to analyze the impact of machine breakdowns, predictive maintenance and product quality failure on system performance. Then, based on the obtained system production loss, a PM decision model is established to minimise the maintenance and production costs, and the optimal maintenance policy is exploited based on an approximate dynamic programming algorithm. In addition, downtime bottleneck (DT-BN) is defined, and a data-driven bottleneck indicator is derived. A continuous improvement method is established through the identification and mitigation of the bottlenecks. Finally, numerical case studies are performed to validate the effectiveness of the proposed PM decision model and continuous improvement method.

Innovative System for Scheduling Production Using a Combination of Parametric Simulation Models

The article deals with the design of an innovative system for scheduling piece and small series discrete production using a combination of parametric simulation models and selected optimization methods. An innovative system for solving production scheduling problems is created based on data from a real production system at the workshop level. The methodology of the innovative system using simulation and optimization methods deals with the sequential scheduling problem due to its versatility, which includes several production systems and due to the fact that in practice, several modifications to production scheduling problems are encountered. Proposals of individual modules of the innovative system with the proposed communication channels have been presented, which connect the individual elements of the created library of objects for solving problems of sequential production scheduling. With the help of created communication channels, it is possible to apply individual parameters of a real production system directly to the assembled simulation model. In this system, an initial set of optimization methods is deployed, which can be applied to solve the sequential problem of production scheduling. The benefit of the solution is an innovative system that defines the content of the necessary data for working with the innovative system and the design of output reports that the proposed system provides for production planning for the production shopfloor level. The DPSS system works with several optimization methods (CR—Critical Ratio, S/RO—Slack/Remaining Operations, FDD—Flow Due Date, MWKR—Most Work Remaining, WSL—Waiting Slack, OPFSLK/PK—Operational Flow Slack per Processing Time) and the simulation experiments prove that the most suitable solution for the FT10 problem is the critical ratio method in which the replaceability of the equipment was not considered. The total length of finding all solutions by the DPSS system was 1.68 min. The main benefit of the DPSS system is the combination of two effectively used techniques not only in practice, but also in research; the mentioned techniques are production scheduling and discrete computer simulation. By combining techniques, it is possible to generate a dynamically and interactively changing simulated production program. Subsequently, it is possible to decide in the emerging conditions of certainty, uncertainty, but also risk. To determine the conditions, models of production systems are used, which represent physical production systems with their complex internal processes. Another benefit of combining techniques is the ability to evaluate a production system with a number of emerging problem modifications.

Manufacturing Execution System Integration through the Standardization of a Common Service Model for Cyber-Physical Production Systems

Digital transformation and artificial intelligence are creating an opportunity for innovation across all levels of industry and are transforming the world of work by enabling factories to embrace cutting edge Information Technologies (ITs) into their manufacturing processes. Manufacturing Execution Systems (MESs) are abandoning their traditional role of legacy executing middle-ware for embracing the much wider vision of functional interoperability enablers among autonomous, distributed, and collaborative Cyber-Physical Production System (CPPS). In this paper, we propose a basic methodology for universally modeling, digitalizing, and integrating services offered by a variety of isolated workcells into a single, standardized, and augmented production system. The result is a reliable, reconfigurable, and interoperable manufacturing architecture, which privileges Open Platform Communications Unified Architecture (OPC UA) and its rich possibilities for information modeling at a higher level of the common service interoperability, along with Message Queuing Telemetry Transport (MQTT) lightweight protocols at lower levels of data exchange. The proposed MES architecture has been demonstrated and validated in several use-cases at a research manufacturing laboratory of excellence for industrial testbeds.

Graph-matrix modeling of production systems as a basis for managing the production capacity of metal working enterprises

Development of information systems and technologies makes it possible to use complex models for information support of management decisions. The most particular interest shows the theory of graphs and matrix calculus in order to assess the production capacity of an industrial enterprise. The study of the authors is devoted to development of methodological approaches to assessing of production capacity based on graph-matrix models. The essence of these models lies in the fact that a manufacturing enterprise is depersonalized by perceiving it as a kind of production system, consisting of production links that add up to a production chain and form a kind of production network, which is represented as a model of the production cycle. In order to perform the appropriate calculations, the specified graph model is linked to the matrix model, which takes into account the main parameters of the production system: technological connections; assortment structure of products; direct consumption coefficients; production capacity of the links. Suggested approach determines the flexibility and broad analytical capabilities of the proposed models for calculating of production capacities. The basis for theoretical and methodological researches became the methods of analysis and synthesis, principles of consistency and complexity, graph theory and matrix calculus. The approbation of the proposed graph-matrix models of production systems was carried out using the examples of a metal working enterprise. The theoretical and practical significance of the study lies in the fact that the proposed graph-matrix model makes it possible to ensure effective management of production facilities by taking into account the mobility of the product assortment structure and complex technological links.

Dynamic risk modeling of complex hydrocarbon production systems

This study presents a dynamic risk modeling strategy for a hydrocarbon sub-surface production system under a gas lift mechanism. A data-driven probabilistic methodology is employed to conduct a risk analysis. The integrated approach comprises a multilayer perceptron (MLP) – artificial neural network (ANN) model and a Bayesian network (BN) technique. The MLP-ANN model performs the production forecast, and the BN model analyzes dynamic risks (the production response) and evaluates the impact of the sand face pressure on risks. The introduced model offers an effective strategy to avoid production failure and to monitor dynamic risks. The dynamic risk analysis yields predictive outcomes at any production time in the well’s production life. It offers field operators an early warning system based on the Bayesian model with prognostic capabilities. The proposed strategy effectively manages production risks and assists in production decision-making, especially in complex production systems.