A prediction approach of SLM based on the ensemble of metamodels considering material efficiency, energy consumption, and tensile strength

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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Possibilities Reducing of Energy Consumption by Cast Iron Production in Foundry

Materials Science Forum ◽

10.4028/www.scientific.net/msf.998.36 ◽

2020 ◽

Vol 998 ◽

pp. 36-41

Author(s):

Peter Futaš ◽

Alena Pribulová ◽

Marcela Pokusova

Keyword(s):

Cast Iron ◽

Energy Consumption ◽

Iron Production ◽

Hot Metal ◽

Metal Production ◽

Material Efficiency ◽

Electric Induction ◽

Wide Range ◽

The Cost ◽

Metal Melting

Modern metal melting includes of cast iron production in different types furnaces with specific characteristics. Furnaces usually adopted are cupola and induction furnaces. Casting cast iron is a manufacturing process characterized by its energy-intensive nature (ie, the use of large amounts of energy per unit of product for main activities) and a long tradition. An example of the energy balance in a foundry is the design of procedures to reduce energy consumption. The most important is the consumption of energy in the production of hot metals (52%), therefore reducing the cost of preparing hot metal is especially important by reducing the energy consumption of metal melting. The most important energy cost practices are the consumption of hot metal to produce 1mt of high quality castings (often 1700 kg) and reduce the energy consumption of hot metal production that varies over a wide range (from 500 to 1300 kWh/mt). Although scientific and technological aspects are now well established, new studies seem to be needed to describe "foundry of the future", where energy and material efficiency is of great importance to ensure competitiveness alongside environmental protection. The paper presents specific procedures for reducing both economically important indicators in cupola and electric induction furnaces.

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Sustainable Analysis of Process Parameters during MIG Welding of 1018 Mild Steel

International Journal of Social Ecology and Sustainable Development ◽

10.4018/ijsesd.293255 ◽

2022 ◽

Vol 13 (1) ◽

pp. 0-0

Keyword(s):

Tensile Strength ◽

Energy Consumption ◽

Mild Steel ◽

Process Parameters ◽

Negative Impact ◽

Sustainable Manufacturing ◽

Welding Process ◽

Travel Speed ◽

Contour Plot ◽

Mig Welding

The present work analyses MIG in terms of strength and consumption of energy during joining of similar AISI 1018 Mild Steel plates. Sustainable manufacturing is the creation of various manufactured products that generally use different processes that will minimize negative impact on environment, conserve natural resources and energy, are also safe for the employees, consumers and communities as well as economically sound. Sustainable manufacturing highlights on the necessity of an energy effective process that optimize consumption of energy. AISI 1018 mild steel is extensively used in automotive industries for pins, worms, dowels gears, non-critical tool components etc. Main important output responses are Tensile Strength and energy consumption during MIG Welding Process by taking Current, Travel Speed and Voltage as effective input variables. The main objective is to optimize energy consumption as well as tensile strength also determination of main influential process parameters on energy Consumption and tensile strength by using Taguchi Method. Contour plot has been also shown.

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Energy consumption modeling and prediction of the milling process: A multistage perspective

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405416682278 ◽

2016 ◽

Vol 232 (11) ◽

pp. 1973-1985 ◽

Cited By ~ 4

Author(s):

Chaoyong Zhang ◽

Zhiheng Zhou ◽

Guangdong Tian ◽

Yang Xie ◽

Wenwen Lin ◽

...

Keyword(s):

Gene Expression ◽

Energy Consumption ◽

Gene Expression Programming ◽

Milling Process ◽

Quadratic Model ◽

Neighborhood Search ◽

Accurate Estimation ◽

Modeling And Prediction ◽

Prediction Approach ◽

Energy Consumption Modeling

In order to provide an accurate estimation of energy consumption, this work proposes a novel energy consumption modeling and prediction approach for a milling process from a multistage perspective. Based on its work stages, each stage’s energy consumption model is established by sliding filter, multiple linear regression, and improved gene expression programming (variable neighborhood search–based gene expression programming) methods and then the total energy consumption is predicted through their combination. A case study is given to illustrate the proposed model and its effectiveness. Compared with the full quadratic model, which can fully consider the interaction between cutting factors, the proposed method can achieve the higher accuracy to predict the energy consumption of the milling process.

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Cohesive Strength in Guayule Rubber and Its Improvement through Chemical Promotion

Rubber Chemistry and Technology ◽

10.5254/1.3535773 ◽

1978 ◽

Vol 51 (5) ◽

pp. 863-871 ◽

Cited By ~ 13

Author(s):

L. F. Ramos de Valle ◽

M. Montelongo

Keyword(s):

Tensile Strength ◽

Energy Consumption ◽

Natural Rubber ◽

Tensile Modulus ◽

Cohesive Strength ◽

Elongation At Break ◽

Green Strength ◽

The Difference

Abstract Green (cohesive) strength of Guayule rubber (GR) was determined and compared to that of natural rubber (NR). In the case of GR gum compounds the green strength was shown to be inferior to that of NR gum compounds; however, in the case of black compounds GR green strength was similar to that of NR. Green strength was very much improved in both rubbers by the addition of MNNA (N-(2-methyl-2-nitropropyl)-4-nitroso aniline) for gum and black compounds. MNNA also acted as an accelerator and thus reduced the difference in vulcanization rate between NR and GR. When using MNNA, a reduction in energy consumption during mastication was also noted. Finally, MNNA notably increased the tensile modulus and decreased the elongation at break for GR vulcanizates, while maintaining an almost constant tensile strength.

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Modeling to predict thermal aging for flame-retardant fabrics considering thermal stability under fire exposure

Textile Research Journal ◽

10.1177/00405175211013835 ◽

2021 ◽

pp. 004051752110138

Author(s):

Xiaohan Liu ◽

Miao Tian ◽

Yunyi Wang ◽

Yun Su ◽

Jun Li

Keyword(s):

Tensile Strength ◽

Flame Retardant ◽

Thermal Aging ◽

Fire Exposure ◽

Ann Model ◽

Degradation Temperature ◽

Ann Models ◽

Artificial Neural Network Ann ◽

Prediction Approach ◽

Method R

The performance of firefighters’ clothing will deteriorate due to various exposures. Predicting its service life before decommissioning is essential to guide the use and maintenance of the uniform. The aim of this study is to introduce a model to predict the tensile strength of flame-retardant fabrics under fire exposure. The thermal degradation and microstructure of Kevlar/polybenzimidazole and polyimide/Kevlar fabrics were investigated. The decrease of tensile strength was attributed to the chemical changes and the development of microstructure cracks and charring of the fibers. Multiple linear regression (MLR) and artificial neural network (ANN) models were established to predict the tensile strength after thermal aging. The ANN model presented a better prediction result ( R2 = 0.88, root mean square error (RMSE) = 96.91) than the MLR method ( R2 = 0.76, RMSE = 138.61). The addition of fabric backside temperature ( T), glass transition temperature ( Tg), and degradation temperature ( Td) further increased the R2 (4%) and decreased the RMSE (14.99) of the ANN model, which was recommended as a prediction approach with better accuracy. The findings of this study will contribute to estimating the continuous performance of firefighting clothing.

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Rice Husk Ash and Fly Ash Effects on the Mechanical Properties of Concrete

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3363 ◽

2020 ◽

Vol 10 (2) ◽

pp. 5402-5405 ◽

Cited By ~ 5

Author(s):

N. Bheel ◽

M. A. Jokhio ◽

J. A. Abbasi ◽

H. B. Lashari ◽

M. I. Qureshi ◽

...

Keyword(s):

Tensile Strength ◽

Compressive Strength ◽

Energy Consumption ◽

Fly Ash ◽

Rice Husk ◽

Carbon Dioxide Emissions ◽

Rice Husk Ash ◽

Split Tensile Strength ◽

Cement Production ◽

Indirect Tensile

Cement production involves high amounts of energy consumption and carbon dioxide emissions. Pakistan is facing a serious energy crisis and cement’s cost is increasing. In addition, landfilling of potential concrete components can lead to environmental degradation. The use of waste as cement replacement not only reduces cement production cost by reducing energy consumption, but it is also environmentally friendly. The purpose of this study is to analyze the characteristics of concrete by partially replacing cement with Rice Husk Ash (RHA) and Fly Ash (FA). This study is mainly focused on the performance of concrete conducting a slump test, and investigating indirect tensile and compressive strength. Cement was replaced with RHA and FA by 5% (2.5% RHA + 2.5% FA), 10% (5% RHA + 5% FA), 15% (7.5% RHA + 7.5% FA) and 20% (10% RHA+10% FA) by weight. Ninety concrete samples were cast with mix proportions of 1:2:4 and 0.55 water/cement ratio. Cube and cylindrical samples were used for measuring compressive and split tensile strength respectively, after 7 and 28 days. The results showed that after 28 days, the 5% RHA+5% FA sample’s compressive strength was enhanced by 16.14% and its indirect tensile strength was improved by 15.20% compared to the conventional sample. Moreover, the sample’s slump value dropped as the content of RHA and FA increased.

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An investigation on energy consumption and part quality of stereolithography apparatus manufactured parts

Rapid Prototyping Journal ◽

10.1108/rpj-06-2020-0143 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Jizhuang Hui ◽

Zhiqiang Yan ◽

Jingxiang Lv ◽

Yongsheng Liu ◽

Kai Ding ◽

...

Keyword(s):

Tensile Strength ◽

Energy Consumption ◽

Layer Thickness ◽

Laser Power ◽

Dimensional Accuracy ◽

Electrical Energy ◽

Design Stage ◽

Content Type ◽

Part Quality

Purpose This paper aims to investigate the influences of process parameters on part quality, electrical energy consumption. Moreover, the relationship between part quality and energy consumption of UTR9000 photosensitive resin fabricated by stereolithography apparatus (SLA) was also assessed. Design/methodology/approach Main effect plots and contour maps were used to analyze the interactions and effects of various parameters on energy consumption and part quality, respectively. Then, a growth rate was used defined as the percentage of the value of energy consumption (or the part quality) of the sample compared to the minimum value of the energy consumption (or the same part quality), to jointly analyze relationships between part quality and energy consumption on a specific process parameter. Findings The part qualities can be improved with increased energy consumption via adjusting layer thickness, without further increasing energy consumption through adjusting laser power, over-cure and scanning distance. Energy consumption can be highly saved while slightly decreasing the tensile strength by increasing layer thickness from 0.09 mm to 0.12 mm. Energy consumption and surface roughness can be decreased when setting laser power near 290 mW. Setting an appropriate over-cure of about 0.23 mm will improve tensile strength and dimensional accuracy with a little bit more energy consumption. The tensile strength increases nearby 5% at a scanning distance of 0.07 mm compared to that at a scanning distance of 0.1 mm while the energy consumption only increases by 1%. Originality/value In this research, energy consumption and multiple part quality for SLA are jointly analyzed first to accelerate the development of sustainable additive manufacturing. This can be used to assist designers to achieve energy-effective fabrication in the process design stage.

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Effect of Sugarcane Bagasse Ash and Lime Stone Fines on the Mechanical Properties of Concrete

Engineering, Technology & Applied Science Research ◽

10.48084/etasr.3434 ◽

2020 ◽

Vol 10 (2) ◽

pp. 5534-5537 ◽

Cited By ~ 1

Author(s):

N. Bheel ◽

A. S. Memon ◽

I. A. Khaskheli ◽

N. M. Talpur ◽

S. M. Talpur ◽

...

Keyword(s):

Tensile Strength ◽

Energy Consumption ◽

Sugarcane Bagasse ◽

Hardened Concrete ◽

Split Tensile Strength ◽

Cement Production ◽

Optimum Result ◽

Mix Proportion ◽

Sugarcane Bagasse Ash ◽

Limestone Fines

Cement production releases huge amounts of carbon dioxide having a significant impact on the environment while also having huge energy consumption demands. In addition, the disposal and recovery of natural concrete components can lead to environmental degradation. The use of waste in concrete not only reduces cement production, but it also reduces energy consumption. The aim of this study is to evaluate the properties of fresh and hardened concrete by partially replacing cement with sugarcane bagasse ash (SCBA) and limestone fines (LSF). In this investigation work the cement was replaced with SCBA ash and LSF by 0% (0% SCBA+ 0% LSF), 5% (2.5% SCBA+ 2.5% LSF), 10% (5% SCBA+ 5% LSF), 15% (7.5% SCBA+ 7.5% LSF) and 20% (10% SCBA+ 10% LSF) by weight of cement. In this regard, a total of 60 samples of concrete specimens were made with mix proportion of 1:1.5:3 with 0.56 water-cement ratio. Cube specimens were tested for compressive strength and cylindrical specimens were used for determining splitting tensile strength at 7 and 28 days respectively. The optimum result displayed that the crushing strength and split tensile strength increased by 10.33% and 10.10% while using 5% SCBA+ 5% LSF as a substitute for cement in concrete after the 28th day. The slump value of concrete declined as the content of SCBA and LSF increased.

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A Novel Prediction Approach for Short-Term Renewable Energy Consumption in China Based on Improved Gaussian Process Regression

Energies ◽

10.3390/en12214181 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4181 ◽

Cited By ~ 1

Author(s):

Huang ◽

Yang ◽

Gao ◽

Jiang ◽

Dong

Keyword(s):

Energy Source ◽

Renewable Energy Source ◽

Renewable Energy ◽

Energy Consumption ◽

State Transition ◽

Energy Structure ◽

Renewable Energy Consumption ◽

State Transition Algorithm ◽

The Government ◽

Prediction Approach

Energy consumption issues are important factors concerning the achievement of sustainable social development and also have a significant impact on energy security, particularly for China whose energy structure is experiencing a transformation. Construction of an accurate and reliable prediction model for the volatility changes in energy consumption can provide valuable reference information for policy makers of the government and for the energy industry. In view of this, a novel improved model is developed in this article by integrating the modified state transition algorithm (MSTA) with the Gaussian processes regression (GPR) approach for non-fossil energy consumption predictions for China at the end of the 13th Five-Year Project, in which the MSTA is utilized for effective optimization of hyper-parameters in GPR. Aiming for validating the superiority of MSTA, several comparisons are conducted on two well-known functions and the optimization results show the effectiveness of modification in the state transition algorithm (STA). Then, based on the latest statistical renewable energy consumption data, the MSTA-GPR model is utilized to generate consumption predictions for overall renewable energy and each single renewable energy source, including hydropower, wind, solar, geothermal, biomass and other energies, respectively. The forecasting results reveal that the proposed improved GPR can promote the forecasting ability of basic GPR and obtain the best prediction effect among all the other comparison models. Finally, combined with the forecasting results, the trend of each renewable energy source is analyzed.

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