Industrial Energy Consumption Research Articles

Sustainable utilization of alkali-activated steel slag material: Effects of silicate modulus and GBFS on fresh, mechanical and pore structure properties

To improve the sustainable utilization of alkali-activated steel slag material, experiments were conducted to systematically investigate effects of sodium silicate modulus and GBFS content on the setting time, workability, mechanical properties and pore structure of alkali-activated steel slag cementitious material. Results show that the setting time is prolonged and workability is reduced as silicate modulus increases. The setting time is first shortened and then prolonged and workability is slightly reduced as GBFS content increases. Increasing silicate modulus and adding GBFS have positive effects on the development of elastic modulus, compressive and splitting tensile strength of alkali-activated steel slag material due to more refined pore structure. The highest compressive strength at 28 d can reach C35 strength level. Manufacturing alkali-activated steel slag material can achieve low energy consumption and carbon emission. If chemical activators can be replaced by some industrial wastes, cost, energy consumption and carbon footprint will be further decreased.

CO2 synergistic emission reduction and health benefits of PM2.5 reaching WHO-III level in Pearl River Delta

Protecting human health is one of the fundamental goals of continuous air quality improvement. Considering the same sources of CO2 and air pollutants emissions, their emission control measures often have a certain degree of synergistic emission reduction effects To balance the costs and policy efficiency, policy makers should assess the synergistic benefits of CO2 emission reduction when formulating air quality improvement strategies. Aimed at the air quality improvement strategy targeting WHO-III level (i.e. 15 μg/m3) for PM2.5 in the Pearl River Delta (PRD) region, this study evaluates the synergistic CO2 reduction effects and the associated health benefits from this strategy which includes a range of air pollution prevention and control measures. The results of the study show that promoting the attainment of WHO-III for PM2.5 in the PRD region can bring about 74 Mt of synergistic CO2 emission reduction, resulting in an 18% reduction of CO2 emissions in the PRD compared to 2017. Among various measures considered, industrial restructuring, power supply transition and industrial energy consumption transformation exhibit the most pronounced synergistic effects. Therefore, these measures are recommended to be prioritized and promoted in the next stage of air pollution prevention and CO2 emission reduction. Furthermore, when the PM2.5 concentration in the PRD region reaches the WHO-III level, the number of PM2.5-related deaths will be estimated to reduce approximately 5.5 thousand compared to that in the current policy scenario. Through continuous structural transformation and emission reduction efforts, it not only facilitates the decline in regional PM2.5 concentration but also helps more regional residents to live in an environment with a relatively low PM2.5 concentration. In addition, in order to reduce the health impacts of PM2.5, it is recommended that the government should guide people to change their production and living styles in order to reduce pollutant emissions from anthropogenic activities.

A novel foaming-sintering technique for developing eco-friendly lightweight aggregates for high strength lightweight aggregate concrete

To address the scarcity of natural aggregates and reduce energy consumption in the concrete industry, a sustainable lightweight aggregate (LWA) was prepared with a self-foaming and sintering method by utilizing waste glass powder as the primary precursor and incineration bottom ash as a foaming agent. The effect of NaOH content (0 M, 2 M, and 8 M) on the physical properties of LWA was investigated. Replacing manufactured sand with sintered LWA (0 M-LWA and 2 M-LWA) by 50 vol% and 100 vol% to investigate their impact on the physical properties, mechanical properties, shrinkage, and alkali-silica reaction of high strength LWA concrete. The high strength LWA concrete prepared with 0 M-LWA possessed a density of 2127 kg/m3 and compressive strength of 108.7 MPa, which were slightly lower than that with manufactured sand. Due to the porous and lightweight nature of 2 M-LWA, the density and compressive strength of LWA concrete significantly decreased with the increased 2 M-LWA volume. Nevertheless, 100%-2M showed satisfying density of 1835 kg/m3 and compressive strength of 50 MPa. The internal curing effect of 2 M-LWA effectively mitigated the autogenous shrinkage of LWA concrete. The alkali-silica reaction expansion of all LWA was innocuous.

The “energy rebound effect” within the framework of environmental sustainability

AbstractThe energy rebound effect, characterized by increasing energy use despite enhanced efficiency, has emerged as a complex interdisciplinary topic in the literature, reflecting its significance in both production and consumption sustainability. Recognizing the pivotal role of academic research in shaping energy management strategies and policies, this study conducted a comprehensive bibliometric review, accessing 530 articles from major citation indexes. This analysis provides insights into the evolution of the energy rebound field and potential future directions. Understanding the existing literature on the rebound effect serves as a valuable foundation for forthcoming research endeavors, addressing the intricate challenge of reconciling energy efficiency with sustainable energy consumption and production. The review explores the extensive literature on the energy rebound effect, highlighting a predominant focus on residential (e.g., cooling, heating, lighting, transportation) and industrial energy consumption in the majority of studies. Empirical applications primarily emphasize the measurement of the energy rebound effect, with a notable trend of increasing research concentration in China in recent years. Additionally, there is a growing body of economy‐wide analyses in this field. However, the literature exhibits complexity in both the analyses conducted and the methods employed for measuring the rebound effect. Research gaps are apparent, numerous studies tend to concentrate solely on the rebound effect at the overall energy consumption level, neglecting variations in energy types and the behavioral patterns of economic actors. Furthermore, despite consistent findings of a higher rebound effect in developing nations, there is a noticeable lack of scholarly literature addressing this issue about these countries.This article is categorized under: Sustainable Energy > Energy Efficiency Human and Social Dimensions > Behavioral Science Policy and Economics > Governance and Regulation

Electrochemical synthesis in company with hydrogen production via renewable energy: Opportunities and challenges

Organic electromechanical synthesis is an eco-friendly and efficient method for material synthesis, effectively addressing the high energy consumption and pollution problems in the traditional chemical industry. By combining hydrogen production from water electrolysis with organic electromechanical synthesis, the reactive oxygen/hydrogen from water hydrolysis can be utilized to oxidize/reduce organic compounds, reducing energy consumption and producing valuable organic products. However, this strategy still faces challenges when implemented in the industry. This paper addresses major technical challenges in the field, providing new insights for future advancements. Firstly, when selecting anode reactions for hydrogen production, it is important to consider the value and market demand of the oxidation product to match the production scale. Secondly, the development of efficient electrocatalysts and electrodes is required to enhance the oxidation kinetics and mass transfer of organics at the current density levels of industrial hydrogen production (500–2000 mA cm−2). Thirdly, it is essential to improve the selectivity and Faraday efficiency of the anode target product to lower the cost of subsequent separation and purification. Fourthly, existing anion and oxygen ion exchange membranes lack corrosion resistance to organic matter, and new separator materials with high ion conductivity and stability are crucial for the electrolytic coupling system. Finally, when combining organic oxidation and water electrolysis, the complexity of product separation increases, and it is recommended to integrate distillation, extraction, membrane separation, and electrochemical reactions to improve process efficiency.

ENERGY INTENSITY OF THE POLISH AGRI-FOOD SECTOR IN THE LIGHT OF INPUT-OUTPUT TABLES

The purpose of this paper is to assess the energy intensity of production in the Polish agri-food (agribusiness) sector, split into agriculture, food industry and supply subsectors, in the light of input-output tables. The study relies on input-output tables and physical energy flow accounts. Energy plays a key role in today’s society as it affects economic growth and the standards of living, while also being at the core of international conflicts, mainly caused by its limited resources. Compared to other sectors, agribusiness demonstrates particularly high levels of energy consumption. Agriculture, which is part of it, has a large share in the consumption of fuels derived from crude oil. This paper presents the consumption of energy in each of the four aggregates of agribusiness, as well as their GDP (in PPS) and energy consumption mix (split into 6groups of energy carriers). The authors found that between2014 and2019, energy consumption in the agribusiness industry increased by ca.9% and was accompanied by ca.8% growth in GDP. The sector’s energy intensity was half more than the average level for the economy as a whole, and remained at ca.5.6TJ per EUR1million of GDP in PPS. Only the food industry was at a level of energy efficiency similar to that of the economy as a whole, whereas the agricultural supply subsector was the only one to demonstrate lower levels. Both the economy as a whole and the agribusiness sector clearly tend to reduce the share of coal and related products in the energy mix.

How population aging promotes carbon emission reduction in Chinese cities: The chain mediating role of industrial upgrading and energy conservation

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities.

Deep Learning-Based Carbon Emission Forecasting and Peak Carbon Pathways in China’s Logistics Industry

As a major energy-consuming industry, energy conservation and emission reduction in the logistics industry are critical to China’s timely achievement of its dual-carbon goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060. Based on deep learning, Random Forest (RF) was used to screen out the key factors affecting carbon emissions in the logistics industry, and the Whale Algorithm-optimized Radial Basis Function Neural Network (WOA-RBF) was proposed. The Monte Carlo simulation predicted the future evolution trends of each key factor under the three scenarios of baseline scenario (BAU), policy regulation scenario (PR), and technological breakthrough scenario (TB) and accurately predicted the carbon emission trends of the logistics industry from 2023 to 2035 by using the most probable future values of each influencing factor as inputs to the WOA-RBF prediction model. The results of the study demonstrate that fixed asset investment (LFI), population (P), total energy consumption (E), energy consumption per unit of value added of the logistics industry (EIL), share of oil consumption (OR), and share of railway freight turnover (RTR) are the key factors influencing the logistics industry’s carbon emissions. Monte Carlo simulations can effectively reflect the uncertainty of future changes in these key factors. In comparison to the BAU and PR scenarios, the TB scenario, with the combined incentives of national policy regulation and technology innovation, is the most likely for the logistics industry to meet the “Peak Carbon” goal baseline scenario.

Predictive modeling of carbon emissions in Jiangsu Province's construction industry: An MEA-BP approach

Understanding and predicting carbon emissions in the construction industry is crucial for sustainable development planning. In this study, we employed the GM(1,1) model and the Mind Evolution Algorithm (MEA) to optimize the BP neural network model and forecast carbon emissions in Jiangsu Province from 2022 to 2026. Using data related to energy consumption and building material usage in the Jiangsu construction industry from 1995 to 2021, we estimated carbon emissions using the life cycle assessment method and IPCC carbon emission coefficient method. Additionally, we utilized the STIRPAT model and Principal Component Analysis (PCA) to select and reduce important variables, identifying three principal components. Subsequently, the MEA-BP model was employed to provide detailed forecasts of carbon emissions over the next five years, revealing a declining trend in carbon emissions from the construction industry. The study thoroughly analyzed the direct and indirect impacts of population growth, economic development, and industrial restructuring on carbon emissions, proposing targeted policy recommendations including sustainable urban planning and green building design. Future research could enhance the applicability and accuracy of the model by expanding the dataset and considering more uncertainty factors. This research offers valuable insights into the trends and influencing factors of carbon emissions in the construction industry of Jiangsu Province, providing guidance for policymakers and future research directions.

Energy and Circular Economy: Nexus beyond Concepts

Energy and materials are increasingly important in industrialized countries, and they impact the economy, sustainability, and people’s future. The purpose of this work was to study the relationship between energy and the circular economy using methods such as Pearson’s correlation and a principal component analysis. Thus, 12 strong correlations were found, with 5 of them between the following relevant variables from two different subjects: the correlations of the raw material consumption, the domestic material consumption, and the material import dependency with the final energy consumption in transport (0.81, 0.92, and 0.81); the correlation of the circular material use rate with the final energy consumption in households (0.70); and the correlation of the material import dependency with the final energy consumption in industry (0.89). The time series forecast was only conclusive for the waste generated, showing that it will increase in the next 10 years.

Mechanism testing of the empowerment of green transformation and upgrading of industry by the digital economy in China

With the continuous advancement of industrialization, global environmental problems are becoming increasingly severe. Maintaining economic growth while improving the environment has been an important issue for many countries, especially developing countries. As industry is a major source of environmental pollution, industrial green transformation and upgrading have become particularly important. In the era of the digital economy (DE), there is a new path for industrial green transformation and upgrading. Based on provincial data on industry from 2008 to 2021, a difference-in-differences (DID) model was constructed to analyze the environmental and economic benefits. New pathways for trade-offs between environmental improvement and economic growth in China are presented. In addition, new ideas are concerning global environmental issues and economic issues in the DE are presented. The present study indicates that the DE has reduced the intensity of pollution emissions and elevated total factor productivity (TFP), which has helped to promote industrial green transformation and upgrading. Further mechanism testing showed that the DE has promoted industrial green transformation and upgrading by improving the utilization of energy and resources and promoting technological innovation. Considering the utilization of energy and resources, the DE has decreased China’s total industrial consumption of energy and coal, reduced industrial water consumption, and reduced the share of coal consumption and increased the share of clean energy consumption in China’s total industrial energy consumption; these effects have optimized the efficiency and structure of China’s energy utilization to contribute to the green transformation and upgrading of industry. Regarding technological innovation, the development of the DE has increased industrial innovation output and R&D input. Furthermore, it has promoted innovation with respect to green processes, accelerating technological innovation, and realized industrial green transformation and upgrading.

Co-pyrolysis of saw dust and oily sludge with waste-heat utilization of steel slag on rotary kiln simulated engineering practice

Pyrolysis of oily sludge (OS) possesses actual problems of high viscosity and high energy consumption in industry. This paper introduced saw dust (SD) to achieve physical viscosity reduction of OS, and then completed OS pyrolysis via steel slag (SS) with sufficient waste heat to replace external heat sources. The rotation of rotary kilns reactor with fins inside were utilized for pyrolysis, which exhibited excellent heat transfer effect for SS heating as validated by discrete element analysis (DEM) simulation. Temperature program and fixed temperature pyrolysis were respectively investigated and the latter one obtained higher yield of oil and gas. Especially SD addition increased the yield and quality of calorific value products (pyrolysis oil and gas). SS not only provided heat source but a lot had catalytic effect on pyrolysis, in which the [O] compounds provided by metal oxides could destroy the stability of chemical bond and deepen the cracking reaction. The energy equilibrium was calculated by heat capacity and reaction energy barrier. Finally, the pyrolytic residue was utilized as soil remediation agent by evaluating Ryegrass growth. Trace FeOx promoted photosynthesis and cellular respiration in plants, while excessive FeOx led to difficulty in respiration and decay of roots. Therefore, this new approach provides an alternative direction for collaborative treatment of OS, SD, and SS with co-benefits from three feedstocks to save energy and produce high-quality products of gas and oil.

Smart traffic routing and service allocation strategy to reduce water consumption in data centers through power reduction

Due to the growth of communication networks, energy consumption in information and communication technology industries is increasing dramatically. Among these industries, data centers are operating with a large number of processors and other components, which, due to heavy processing and mass data transmission, in addition to consuming high electrical power, also cause high thermal losses. Since water cooling systems are used for cooling different parts of these centers as well as for cooling the electric power generation unit, these centers are among the major consumers of water and electricity resources. In this article, while examining the important factors affecting water consumption in data centers, useful methods are suggested to reduce the consumption of electrical energy and water. Optimizing energy consumption in data centers is possible in three parts: routing, servicing and use of cooling equipment. For all three parts, improvement methods are suggested in this article. For this purpose, an optimization problem is designed and an algorithm is presented to solve it. In the proposed solution, an energy-smart method based on SDN technology is used for routing, virtual machines equipped with the possibility of reducing power consumption in no-load conditions are used for servicing, and two types of water-cooled and air-cooled systems are used for cooling equipment. is used. The simulation results show that depending on the cooling system, the proposed method reduces water consumption between 24% and 32% compared to the case where the proposed solution is not used.

Study on industrial carbon emissions in China based on GDIM decomposition method and two decoupling effects.

The driving factors of China's industrial carbon emissions are decomposed by generalized Divisia index method (GDIM), so as to study the reasons for the change of China's industrial carbon emissions. The decoupling effect of China's industrial carbon emissions and economic growth is examined by speed decoupling and quantity decoupling. The speed decoupling is calculated by Tapio decoupling elasticity and emission reduction effort function, and the quantity decoupling is measured by environmental Kuznets curve (EKC). The results show that the positive driving factors are output size effect > industrial energy consumption effect > population size effect, and the negative driving factors are investment carbon emission effect > output carbon intensity effect > per capita output effect > economic efficiency effect > energy intensity effect. The elasticity of emission reduction is basically greater than that of energy conservation, indicating that there is still abundant room for efforts in emission reduction. The overall decoupling effect of carbon emissions is undecoupling-strong decoupling-undecoupling. Quadratic EKC shape is "U" shape, and the inflection point is 11.0987; the shape of cubic EKC is "N," and the inflection points are - 0.0137 and 2.4069, respectively, which satisfies the hypothesis of EKC curve.

Analysis of energy intensity indicators in production of mineral fertilizers

The article presents the results of the calculation of the energy consumption of the main and auxiliary technologies for the production of mineral fertilizers, interrelated as a single energy technology system (ETS). At the stage of the primary analysis of fuel, electricity and heat consumption indicators of individual technologies of the mineral fertilizers industry, the task was set to determine the energy intensity of specific technologies depending on intra-system distribution of flows of fuel and energy resources (FER) between the ETS production facilities for the output of target products.On the basis of a mathematical model, according to the averaged specific consumption rates of fuel and energy resources of the main and auxiliary technologies for the production of one ton of product, the values of specific energy consumption for target technologies for the production of mineral fertilizers were obtained. The analysis of the calculated indicators of energy consumption enabled to identify the most energy-intensive technologies.. It is shown that in the total energy consumption of the target technology, the share of energy consumption of auxiliary industries is up to 97% of the total energy intensity. Besides, the paper cosiders the key factors that determine the level of energy efficiency of industry enterprises under study.Through the example of an existing technology for the production of mineral fertilizers of one technological line with a capacity of 27.5 t/h (in terms of the finished product), the calculation of energy consumption was performed. The paper states that the modernization of the technology for the production of mineral fertilizers through the utilization of thermal SERs emitted into the environment would reduce the energy consumption of the technology by 6.33%.The mathematical model used in the study makes it possible to determine the values of energy consumption of technologies in the mineral fertilizer production industry with the greatest accuracy. The analysis of the data obtained shows that the studied technologies of the industry are characterized by irrational consumption of fuel and energy resources and, therefore, need a more thorough and systematic approach in determining energy saving reserves and ways to rationalize them in the energy technology system (ETS).

Research on Green Building Design Optimization Based on Building Information Modeling and Improved Genetic Algorithm

The energy consumption of the construction industry has been increasing year by year, posing a huge challenge to China’s dual carbon goals of peaking carbon emissions and achieving carbon neutrality. The Chinese construction industry has huge potential for energy conservation and emission reduction, and the government has therefore put forward requirements for constructing green buildings and formulated strict evaluation standards. The carbon emissions of the construction industry involve various stages of the entire life cycle and are closely related to the green building design standards that meet the requirements. This article sets multiple objective functions based on the two dimensions of the carbon emissions of the entire life cycle of buildings and green building evaluation and uses the NSGA-II algorithm in genetic algorithms to optimize ten indicators selected from the two objectives. Based on this, building information modeling (BIM) modeling was carried out for an office building project in Southwest China, and energy consumption analysis and evaluation were conducted based on the project’s multidisciplinary model. The dialectical relationship between the carbon emissions of the entire life cycle of buildings and the green building evaluation values was discovered, and the optimized parameter combination scheme corresponding to the Pareto solution set was obtained, providing a reference for using improved genetic algorithms and BIM technology to optimize green building design.

Pathways for carbon emission prediction and mitigation of sustainable industrial parks: a LEAP model application

ABSTRACT Industrial parks play a crucial role as a carrier of industrial clusters and energy consumption. Accurately predicting the energy demand and carbon emissions trend is key to scientifically determining the pathways for low-carbon industrial parks. However, exploration in carbon emission prediction on industrial park scale is still in its infancy stage. This paper investigates fuel demand and carbon emissions from 2021 to 2035 in an industrial park in Jiangsu Province, utilizing the Long-range Energy Alternative Planning (LEAP) model to explore the pathways for low carbon development. Energy-saving and emission-reduction effects of different macro-economic policies and micro-energy planning are analyzed based on the energy balance and emission factor methods. Four scenarios are compared: the baseline scenario (BAS), green development scenario (GDS), low carbon scenario (LCS), and strength low carbon scenario (SLS). Results indicated that energy demand under BAS reached at 31.37 Mtce in 2035, and energy-saving rates of GDS, LCS, and SLS in 2035 were 12.94%, 14.00% and 19.08%, respectively. Carbon emissions reached 53.96 MtCO2e in BAS of 2035. However, in the same year, emissions decreased by 24.88%, 43.09%, and 52.52% in GDS, LCS, and SLS, respectively, with SLS being the most suitable for the park.

Research on Regional Industrial Layout and Structural Adjustment and Upgrading under Digital Economy

Digital transformation promotes the change of urban governance. Based on the concept of "system", this paper focuses on exploring the influencing factors and evaluation indicators of the industrial structure adjustment and energy production and consumption structure adjustment of green smart cities in the new era under the promotion of digital economy, providing a strong theoretical basis for the comprehensive evaluation of the influencing factors in the later period.

Research on the impact and intermediary effect of carbon emission trading policy on carbon emission efficiency in China

In this paper, the impact of the carbon emission trading policy on carbon emission efficiency is discussed based on the local data of China from 2004 to 2019, based on the natural experiment of carbon emission trading policy. The intermediary model is introduced to analyze the mediating effect of industrial structure and energy consumption structure in emission reduction. The results show that the carbon emission trading policy not only directly improves the carbon emission efficiency, but also indirectly improves the carbon emission efficiency by upgrading the industrial structure and adjusting the energy consumption structure. Finally, this paper verifies that carbon trading policy has a synergistic emission reduction effect on sulfur emission efficiency. The mechanism of the SO2 reduction is similar to that of the CO2 reduction mechanism, with direct and intermediary effects.

Joint torque prediction of industrial robots based on PSO-LSTM deep learning

PurposeBecause of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.Design/methodology/approachThe proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.FindingsThe predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.Originality/valuePSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.