scholarly journals Optimization of Safety Stock under Controllable Production Rate and Energy Consumption in an Automated Smart Production Management

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2059 ◽  
Author(s):  
Mitali Sarkar ◽  
Biswajit Sarkar
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Production System ◽  
Spare Parts ◽  
Energy Utilization ◽  
Smart Manufacturing ◽  
Defective Items ◽  
Safety Stock ◽  
Random Breakdown ◽  
Optimum Energy

A smart production system is essential to produce complex products under the consumption of efficient energy. The main ramification of controllable production rate, amount of production size, and safety stocks is simultaneously optimized under proper utilization of energy within a smart production system with a random breakdown of spare parts. Due to the random breakdown, a greater amount of energy may be used. For this purpose, this study is concerned about the optimum safety stock level under the exact amount of energy utilization. For random breakdown, there are three cases as production inventory meets the demand without utilization of the safety stock, with using of the safety stock, and consumed the total safety stock amount and facing shortages. After the random breakdown time, the smart production system may move to an out-of-control state and may produce defective items, where the production rate of defective items is a random variable, which follows an exponential distribution. The total cost is highly nonlinear and cannot be solved by any classical optimization technique. A mathematical optimization tool is utilized to test the model. Numerical study proves that the effect of energy plays an important role for the smart manufacturing system even though random breakdowns are there. it is found that the controllable production rate under the effect of the optimum energy consumption really effects significantly in the minimization cost. It saves cost regarding the corrective and preventive maintenance cost. The amount of safety stock can have more support under the effect of optimum energy utilization. The energy can be replaced by the solar energy.

scholarly journals Effect of Energy and Failure Rate in a Multi-Item Smart Production System

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2958 ◽  
Author(s):  
Mitali Sarkar ◽  
Biswajit Sarkar ◽  
Muhammad Iqbal
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Failure Rate ◽  
Production System ◽  
Optimal Solution ◽  
Development Cost ◽  
Lot Size ◽  
Defective Items ◽  
Unusual Amount ◽  
Control State

To form a smart production system, the effect of energy and machines’ failure rate plays an important role. The main issue is to make a smart production system for complex products that the system may produce several defective items during a long-run production process with an unusual amount of energy consumption. The aim of the model is to obtain the optimum amount of smart lot, the production rate, and the failure rate under the effect of energy. This study contains a multi-item economic imperfect production lot size energy model considering a failure rate as a system design variable under a budget and a space constraint. The model assumes an inspection cost to ensure product’s quality under perfect energy consumption. Failure rate and smart production rate dependent development cost under energy consumption are considered, i.e., lower values of failure rate give higher values of development cost and vice versa under the effect of proper utilization of energy. The manufacturing system moves from in-control state to out-of-control state at a random time. The theory of nonlinear optimization (Kuhn–Tucker method) is employed to solve the model. There is a lemma to obtain the global optimal solution for the model. Two numerical examples, graphical representations, and sensitivity analysis of key parameters are given to illustrate the model.

scholarly journals A Constrained Production System Involving Production Flexibility and Carbon Emissions

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 275 ◽  
Author(s):  
Asif Iqbal Malik ◽  
Byung Soo Kim
Keyword(s):  
Production Rate ◽  
Carbon Emissions ◽  
Production System ◽  
Ghg Emissions ◽  
Real Life ◽  
Lot Size ◽  
Setup Cost ◽  
Defective Items ◽  
Machine Failure ◽  
Decision Variables

The proposed study presents an economic lot size and production rate model for a single vendor and a single buyer setup. This model involves greenhouse gas (GHG) emissions from industrial sources. The carbon emissions in this model are considered as two types: direct emissions and indirect emissions. The production rate affects carbon emissions generation in production, i.e., generally, higher production rates result in more emissions, which is governable in many real-life cases. The production rate also impacts the process reliability and quality. Faster production deteriorates the production system quickly, leading to machine failure and defective items. Such reliability and quality problems increase energy consumptions and supply chain (SC) costs. This paper formulates a vendor-buyer SC model that tackles these issues. It considers two decision-making policies: integrated or centralized as well as decentralized, where the aim is to obtain the optimal values of the decision variables that give the minimum total SC cost. It includes the costs of setup, holding inventory, carbon emissions, order processing, production, reworking, and inspection processes. The decision variables are the production rate, lead time, order quantity, the number of shipments, and the investments for setup cost reduction. In the later sections, this paper compares the numerical outcomes of the two centralized and decentralized policies. It also provides sensitivity analysis and useful insights on the economic and environmental execution of the SC.

scholarly journals Multi-Product Production System with the Reduced Failure Rate and the Optimum Energy Consumption under Variable Demand

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 465 ◽  
Author(s):  
Shaktipada Bhuniya ◽  
Biswajit Sarkar ◽  
Sarla Pareek
Keyword(s):  
Energy Consumption ◽  
Failure Rate ◽  
Production System ◽  
Production Systems ◽  
Hessian Matrix ◽  
Global Optimality ◽  
Long Run ◽  
Decision Variables ◽  
Optimum Energy ◽  
Smart Product

The advertising of any smart product is crucial in generating customer demand, along with reducing sale prices. Naturally, a decrease in price always increases the demand for any smart product. This study introduces a multi-product production process, taking into consideration the advertising- and price-dependent demands of products, where the failure rate of the production system is reduced under the optimum energy consumption. For long-run production systems, unusual energy consumption and machine failures occur frequently, which are reduced in this study. All costs related with the production system are included in the optimum energy costs. The unit production cost is dependent on the production rate of the machine and its failure rate. The aim of this study is to obtain the optimum profit with a reduced failure rate, under the optimum advertising costs and the optimum sale price. The total profit of the model becomes a complex, non-linear function, with respect to the decision variables. For this reason, the model is solved numerically by an iterative method. However, the global optimality is proved numerically, by using the Hessian matrix. The numerical results obtained show that for smart production, the maximum profit always occurs at the optimum values of the decision variables.

scholarly journals Optimization of Laser Engraving of Acrylic Plastics from the Perspective of Energy Consumption, CO2 Emission and Removal Rate

2021 ◽  
Vol 5 (3) ◽  
pp. 78
Author(s):  
Mohammad Muhshin Aziz Khan ◽  
Shanta Saha ◽  
Luca Romoli ◽  
Mehedi Hasan Kibria
Keyword(s):  
Energy Consumption ◽  
Production Rate ◽  
Material Removal Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Scanning Speed ◽  
Gas Emissions ◽  
Laser Engraving ◽  
Co2 Gas ◽  
Optimal Set

This paper focuses on optimizing the laser engraving of acrylic plastics to reduce energy consumption and CO2 gas emissions, without hindering the production and material removal rates. In this context, the role of laser engraving parameters on energy consumption, CO2 gas emissions, production rate, and material removal rate was first experimentally investigated. Grey–Taguchi approach was then used to identify an optimal set of process parameters meeting the goal. The scan gap was the most significant factor affecting energy consumption, CO2 gas emissions, and production rate, whereas, compared to other factors, its impact on material removal rate (MRR) was relatively lower. Moreover, the defocal length had a negligible impact on the response variables taken into consideration. With this laser-process-material combination, to achieve the desired goal, the laser must be focused on the surface, and laser power, scanning speed, and scan gap must be set at 44 W, 300 mm/s, and 0.065 mm, respectively.

scholarly journals Energetic and exergetic analysis of a convective drier: A case study of potato drying process

2020 ◽  
Vol 5 (1) ◽  
pp. 563-572
Author(s):  
Iman Golpour ◽  
Mohammad Kaveh ◽  
Reza Amiri Chayjan ◽  
Raquel P. F. Guiné
Keyword(s):  
Energy Consumption ◽  
Research Work ◽  
Specific Energy Consumption ◽  
Exergy Efficiency ◽  
Operating Conditions ◽  
Energy Utilization ◽  
Convective Drying ◽  
Drying Time ◽  
Energy And Exergy ◽  
Exergy Losses

AbstractThis research work focused on the evaluation of energy and exergy in the convective drying of potato slices. Experiments were conducted at four air temperatures (40, 50, 60 and 70°C) and three air velocities (0.5, 1.0 and 1.5 m/s) in a convective dryer, with circulating heated air. Freshly harvested potatoes with initial moisture content (MC) of 79.9% wet basis were used in this research. The influence of temperature and air velocity was investigated in terms of energy and exergy (energy utilization [EU], energy utilization ratio [EUR], exergy losses and exergy efficiency). The calculations for energy and exergy were based on the first and second laws of thermodynamics. Results indicated that EU, EUR and exergy losses decreased along drying time, while exergy efficiency increased. The specific energy consumption (SEC) varied from 1.94 × 105 to 3.14 × 105 kJ/kg. The exergy loss varied in the range of 0.006 to 0.036 kJ/s and the maximum exergy efficiency obtained was 85.85% at 70°C and 0.5 m/s, while minimum exergy efficiency was 57.07% at 40°C and 1.5 m/s. Moreover, the values of exergetic improvement potential (IP) rate changed between 0.0016 and 0.0046 kJ/s and the highest value occurred for drying at 70°C and 1.5 m/s, whereas the lowest value was for 70°C and 0.5 m/s. As a result, this knowledge will allow the optimization of convective dryers, when operating for the drying of this food product or others, as well as choosing the most appropriate operating conditions that cause the reduction of energy consumption, irreversibilities and losses in the industrial convective drying processes.

Wearable energy efficient fitness tracker for sports person health monitoring application

2021 ◽  
pp. 1-10
Author(s):  
Yongyue Huang ◽  
Min Hu ◽  
BalaAnand Muthu ◽  
R. Gayathri
Keyword(s):  
Energy Consumption ◽  
Data Storage ◽  
Energy Efficient ◽  
Consumer Satisfaction ◽  
Form Factors ◽  
Recognition System ◽  
Energy Utilization ◽  
Biologically Inspired Algorithms ◽  
Enhance Efficiency ◽  
Fitness Tracker

Continuous evaluation of biological and physiological metrics of sports personalities, evaluating general health status, and alerting for life-saving treatments, is supposed to enhance efficiency and healthy performance. Wearable devices with acceptable form factors compact, flexibility, minimal power consumption, etc., are needed for continuous monitoring to avoid affecting everyday operations, thereby retaining functional effectiveness and consumer satisfaction. This research focuses on the acceleration tracker for particularizing the work. Acceleration data is typically collected on battery-powered sensors for activity detection, referring to an exchange between high-precision detection and energy-efficient processing. From a feature selection perspective, the paper explores this trade-off. It suggests an Energy-Efficient Behavior Recognition System with a comprehensive energy utilization model and the Multi-objective Algorithm of Particle Swarm Optimization (EEBRS-MPSO). Therefore, using Random Forest (RF) classifiers, the model and algorithm are tested to measure the precision of identification and obtain the task’s best performance with the lowest energy consumption, among other biologically-inspired algorithms. The findings indicate that energy consumption for data storage and data processing is minimized with magnitude relative to the raw data method by choosing suitable groups of attributes. Thus, the platform allows a scalable range of feature clusters that require the authors to provide an adequate power adjustment for given target use.

scholarly journals Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery

2021 ◽  
Vol 11 (4) ◽  
pp. 1481
Author(s):  
Aleksandra Cichoń ◽  
William Worek
Keyword(s):  
Energy Consumption ◽  
Convective Heat Transfer Coefficient ◽  
Specific Energy Consumption ◽  
Analytical Investigation ◽  
Dew Point ◽  
Energy Utilization ◽  
Coefficient Of Performance ◽  
Air Cooling ◽  
Water Recovery ◽  
Utilization Factor

This paper presents the analytical investigation of a novel system for combined Dew Point Cooling and Water Recovery (DPC-WR system). The operating principle of the presented system is to utilize the dew point cooling phenomenon implemented in two stages in order to obtain both air cooling and water recovery. The system performance is described by different indicators, including the coefficient of performance (COP), gained output ratio (GOR), energy utilization factor (EUF), specific energy consumption (SEC) and specific daily water production (SDWP). The performance indicators are calculated for various climatic zones using a validated analytical model based on the convective heat transfer coefficient. By utilizing the dew point cooling phenomenon, it is possible to minimize the heat and electric energy consumption from external sources, which results in the COP and GOR values being an order of magnitude higher than for other cooling and water recovery technologies. The EUF value of the DPC-WR system ranges from 0.76 to 0.96, with an average of 0.90. The SEC value ranges from 0.5 to 2.0 kWh/m3 and the SDWP value ranges from 100 to 600 L/day/(kg/s). In addition, the DPC-WR system is modular, i.e., it can be multiplied as needed to achieve the required cooling or water recovery capacity.

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