Energy Consumption Of Production System Research Articles

Approach of Model Extension for Virtual Commissioning to Predict Energy Consumption of Production Systems

Approach of Model Extension for Virtual Commissioning to Predict Energy Consumption of Production Systems

Switch-Off Policies in Job Shop Controlled by Workload Control Concept

The reduction in emissions and the increase in energy costs push companies to identify solutions to reduce energy consumption in production systems. One of the approaches proposed in the literature is the shutdown of machines to reduce energy consumption in the idle state. This solution does not affect production processes and can be applied in various manufacturing fields. This paper proposes switch-off policies in manufacturing systems under a workload control system. The shutdown policies developed consider the number of items in the queue and the calculation derived from the workload control mechanism. Simulation models have been developed to test the proposed policies using the case always on as a benchmark, considering different levels of absorbed power in the inactivity and warm-up states and different warm-up times. The results highlight how the switch policies that include the workload evaluation drastically reduce the number of on/off activities, assuring lower energy consumption.

Controllable Energy Consumption in a Sustainable Smart Manufacturing Model Considering Superior Service, Flexible Demand, and Partial Outsourcing

The waste of energy in the present era is a dangerous signal for the future. All categories of consumers should come forward to moderate energy use and prevent wastage. This study focuses on a controllable energy consumption-based sustainable inventory model incorporating variable production rates, improved service, partial outsourcing planning, defective production, restoring reworkable items, disposing of non-reworkable items, and energy-saving steps. Reducing unusual energy consumption in production systems reduces carbon emissions and maximizes the system’s profit. An improved service level attracts customers, increases demand, and improves product reputation. Separate holding costs of reworked, defective, and perfect-quality items are considered for every lot delivered and reworked. The demand in the market is related to price and service. A traditional optimization technique examines the global optimization for the profit function and decision variables. Numerical illustrations as well as concave 3D graphs validate the analytical results and provide a sensitivity analysis for different parameters. The model is validated through special cases and comparison graphs.

A Model for the Intelligent Supervision of Production for Industry 4.0

Nowadays, the automation of production processes requires qualified engineering staff; unfortunately, such personnel are in very short supply in all EU countries. This article proposes a concept for a model for the intelligent supervision of production for Industry 4.0., the implementation of which will allow the demand for highly skilled engineering staff to be reduced within a company. An analysis of the literature, dedicated to manufacturing enterprises, regarding the intelligent supervision of production systems, is carried out in the article. It follows, therefore, that the Industry 4.0 concept assumes that mechanisms will be implemented, in production resources, in order to enable preventive measures to be taken, vis-à-vis breakdowns, failures and disruptions to the operation of devices. The need to develop a model for the intelligent supervision of production systems, in the face of challenges within the concept of Industry 4.0, is proposed. This model includes the following elements: (1) a configurator allowing devices to be selected for measuring production parameters, (2) a database for registering production system parameters, (3) Convolutional Neural Networks (CNN), as the prediction algorithm and (4) a knowledge-based structure, including operating procedures and good practices for preventing emergency situations, threats and excessive energy consumption in production systems. The usefulness of this model for predictive maintenance, safety and energy efficiency, vis-à-vis the use of production resources, as well as in support of middle management for decisions taken by employees, is described.

A data-driven model for energy consumption analysis along with sustainable production: A case study in the steel industry

ABSTRACT Sustainable production is of the most serious concerns that affect production systems. In a manufacturing company, efficient energy consumption, which leads to significant environmental benefits, is an important factor that indicates the performance of sustainable production. This paper proposes a three-stage data-driven model to analyze energy consumption in production systems. The first stage develops energy consumption predictors, the second stage extracts production scenarios, and the last stage predicts the energy consumption for each scenario. We implemented the model in a steel manufacturing plant for investigating the electricity consumption (EC) of Electric Arc Furnace (EAF). First, we developed four groups of predictors where Boosted Neural Network achieved the best result in predicting EAF’s electricity consumption (RMSE = 587, R-Squared = 0.859, MAPE = 0.073). Second, we extracted eight distinct production scenarios based on different amounts of input materials through a descriptive data-mining algorithm, K-means. Third, the EC of production scenarios was predicted by the best predictor. Feature analysis showed that Direct Reduced Iron(DRI), ladle age, and scrap grade-3 have the most effect on predicting EC. Scenario analysis illustrated that scenarios with a higher share of DRI cause a higher amount of EC. Contrastingly, input materials with more share of high-grade scrap types lead to more efficient EC.

Bi-objective optimisation approaches to Job-shop problem with power requirements

Nowadays, a large focus is given to mass personalisation, and multiple path shop floors are suited to such production environments. Hence, this paper deals with the Job-shop scheduling problem that is used for modelling a manufacturing system. Meanwhile, a large attention is given to energy consumption of production systems, but few works consider power requirements of the production systems in order to process operations. In order to contribute in filling this gap, this paper considers the problem where the objective is to minimise both the total completion time of all operations and the instant available power required to process these operations. The problem results in the Bi-objective Job-shop Problem with Power Requirements (Bi-JSPPR). The goal of this paper is to provide a Pareto frontier of schedules minimising both criteria, considering that operations may consume a lot of power at the beginning of the process (consumption peak), more than its consumption after a while, which allows to model power profiles of manufacturing operations. To solve the problem two metaheuristic approaches are investigated: a hybrid Non-dominated Sorting Genetic Algorithm (NSGA-II) and an iterated Greedy Randomized Adaptive Search Procedure coupled with an Evolutionary Local Search (iGRASP×ELS). An efficient local search procedure is specifically designed to improve the quality of solutions in the Pareto frontier of the hybrid NSGA-II (hNSGA-II). Computational experiments and statistical tests are conducted to demonstrate the efficiency of the approaches. Results show that both approach are complementary, having the hNSGA-II showing better average performances, while the iGRASP×ELS is better when high peak power consumption are considered.

Towards Energy Efficient Scheduling of Manufacturing Systems through Collaboration between Cyber Physical Production and Energy Systems

Currently, enhancing sustainability, and in particular reducing energy consumption, is a huge challenge for manufacturing enterprises. The vision of the fourth industrial revolution (so-called “industry 4.0”) is not only to optimize production and minimize costs, but also to reduce energy consumption and enhance product life-cycle management. To address this challenge, a multi-agent architecture aimed at elaborating predictive and reactive energy-efficient scheduling through collaboration between cyber physical production and energy systems is proposed in this paper. Smart, sustainable decision tools for cyber physical production systems (CPPS) and cyber physical energy systems (CPES) are proposed. The decision tools are data-driven, agent-based models with dynamic interaction. The main aim of agent behaviours in the cyber part of CPPS is to find a predictive and reactive energy-efficient schedule. The role of agents in CPES is to control the energy consumption of connected factories and switch between the different renewable energy sources. Dynamic mechanisms in CPPS and CPES are proposed to adjust the energy consumption of production systems based on the availability of the renewable energy. The proposed approach was validated on a physically distributed architecture using networked embedded systems and real-time data sharing from connected sensors in each cyber physical systems. A series of instances inspired from the literature were tested to assess the performance of the proposed method. The results prove the efficiency of the proposed approach in adapting the energy consumption of connected factories based on a real-time energy threshold.

ENERGY COMPONENT OF UKRAINIAN REGIONS’ ECONOMIC SECURITY

The article highlights the basic concepts of energy security of regions. Factors influencing the energy security of the regions are considered, they can be divided into two groups: factors that can be eliminated or localized in the current and strategic period, that are directly in the field of management (these include energy shortages, man-made accidents, financial problems); factors whose management period is based on the time of strategic programs (period of hypotheses) and which are managed only indirectly through the concepts of development (these include limited resources, environmental problems, etc.). The economic threats to the energy security of the regions are highlighted: lack of investment resources necessary for the development, modernization and technical support of the normal operation of the energy complex; financial instability of ensuring the functioning of the energy complex, provision of fuel resources, materials and components to support technological processes, stability of payment of all current costs; breach of economic ties; inefficient use of fuel and material resources; excessively high prices for fuel and material resources; high levels of monopoly of producers, suppliers and distributors of energy and fuel resources; technical constraints arising from lack of funds; imbalance of production and consumption (FER), shortage of energy capacity, insufficient network capacity. The main characteristics of the energy security system of the regions, which should be emphasized in the formation of the management mechanism: energy saving as a characteristic of organizational and technical policy of energy security in the framework of social responsibility of the subjects; energy efficiency as a characteristic of economic policy to ensure the optimal balance of energy supply and energy intensity and energy consumption of production systems; energy competitiveness as a characteristic of political regulation of consumption of energy resources of the region. The mechanism of energy security management of regions, which should be based on the most important principles and their combination, is analyzed.

Knowledge Based and PLM Facilities for Sustainability Perspective in Manufacturing: A Global Approach

The optimization of resources and energy consumption of production system is one of the fields in which industries should make relevant improvements to successfully integrate sustainability aspects in the Factory of the Future (FoF). Due to the complexity of this issue, one of important challenge for the success of any assessment or resource optimization strategy concerns the control and the availability of all relevant data and knowledge along the product lifecycle. The aim of this work is to explore the capabilities of knowledge based frameworks combined to Product Lifecycle Management (PLM) approaches as a backbone for supporting resources optimization within a sustainability perspective.

Simulationsgestützte Bewertung von Energiesparstrategien

Reducing energy consumption of production systems without compromising productivity is a current and ongoing challenge. Technological approaches such as efficient drives have often been implemented already. Powering down inactive machine components during nonproductive periods promises further potential. To operate appropriate control strategies, information about the flow of production may be used. The application of such a flow-oriented approach in a case study, accompanied by a quantified analysis of the results, is presented in this paper.

Energy balance methodology and modeling of supplementary forage production for cattle in Brazil

Cattle is the main protein source in Brazil and cattle production depends on preserving forage in order to decrease the influence of dry periods on grass production. To minimize such problems, some new techniques have been created to increase the yield which also leads to energy demand increase. Energy balance is a vital tool to evaluate the efficiency of energy consumption in production systems. There is no standard methodology established for this determination. It is also difficult to analyze different management options because of the complexity of the production systems and the interactions among variables. Therefore the purpose of this study is to develop a methodology that supports the development of a model, using a spreadsheet, and to use it to analyze the energy balance of production systems. The model was applied to a traditional production system of maize (Zea mays L.) silage and a Bermuda grass (Cynodon spp.) haylage. The gross energy balance presented was 14.1 energy units of output per energy units of input for maize silage and 0.98 for haylage. For the digestible energy balance, the values were 9.1 and 0.99, respectively. The total energy demanded was 74.3% in maize silage fertilizations and 99.7% in haylage irrigation. Yield and dry matter contents were indicated in a sensitivity analysis as the main critical variables for maize, whereas for haylage, it was not possible to indicate any. The best alternative scenarios for improving energy efficiency in maize silage and haylage production were the reductions of fertilizer concentration and irrigation use, respectively.