Technological Energy Efficiency Improvements in Cement Industries

The cement industry is highly energy-intensive, consuming approximately 7% of global industrial energy consumption each year. Improving production technology is a good strategy to reduce the energy needs of a cement plant. The market offers a wide variety of alternative solutions; besides, the literature already provides reviews of opportunities to improve energy efficiency in a cement plant. However, the technology is constantly developing, so the available alternatives may change within a few years. To keep the knowledge updated, investigating the current attractiveness of each solution is pivotal to analyze real companies. This article aims at describing the recent application in the Italian cement industry and the future perspectives of technologies. A sample of plant was investigated through the analysis of mandatory energy audit considering the type of interventions they have recently implemented, or they intend to implement. The outcome is a descriptive analysis, useful for companies willing to improve their sustainability. Results prove that solutions to reduce the energy consumption of auxiliary systems such as compressors, engines, and pumps are currently the most attractive opportunities. Moreover, the results prove that consulting sector experts enables the collection of updated ideas for improving technologies, thus giving valuable inputs to the scientific research.

Industry-energy system management by a Copula-based stochastic programming approach

In this study, a Copula-based stochastic industry-energy system management (CSIE) model was developed based on Copula-based stochastic programming and interval linear programming. CSIE model can not only deal with extreme random events in industry-energy system (IES) of resource-dependent cities, but also quantify the risks of industrial energy demand-supply. To prove the practicability, a case study of IES planning in Yulin city was represented. Reasonable solutions of energy production and industrial energy consumption strategy were obtained, which can guarantee that pollutant emission meets the environmental requirements, and the system cost gets the lowest during 2021-2035. Furthermore, CSIE model could be spread to IES management in similar resource-dependent cities.

Industrial Electrical Energy Consumption Forecasting by using Temporal Convolutional Neural Networks

In conjunction with the 4th Industrial Revolution, many industries are implementing systems to collect data on energy consumption to be able to make informed decision on scheduling processes and manufacturing in factories. Companies can now use this historical data to forecast the expected energy consumption for cost management. This research proposes the use of a Temporal Convolutional Neural Network (TCN) with dilated causal convolutional layers to perform forecasting instead of conventional Long-Short Term Memory (LSTM) or Recurrent Neural Networks (RNN) as TCN exhibit lower memory and computational requirements. This approach is also chosen due to traditional regressive methods such as Autoregressive Integrated Moving Average (ARIMA) fails to capture non-linear patterns and features for multi-step time series data. In this research paper, the electrical energy consumption of a factory will be forecasted by implementing a TCN to extract the features and to capture the complex patterns in time series data such daily electrical energy consumption with a limited dataset. The neural network will be built using Keras and TensorFlow libraries in Python. The energy consumption data as training data will be provided by GoAutomate Sdn Bhd. Then, the historical data of economic factors and indexes such as the KLCI will be included alongside the consumption data for neural network training to determine the effects of the economy on industrial energy consumption. The forecasted results with and without the economic data will then be compared and evaluated using Weighted Average Percentage Error (WAPE) and Mean Absolute Percentage Error (MAPE) metrics. The parameters for the neural network will then be evaluated and fined tuned accordingly based on the accuracy and error metrics. This research is able create a CNN to forecast electrical energy consumption with WAPE = 0.083 & MAPE = 0.092, of a factory one (1) week ahead with a small scale dataset with only 427 data points, and has determined that the effects of economic index such as the Bursa Malaysia has no meaningful impact on industrial energy consumption that can be then applied to the forecasting of energy consumption of the factory.

Importance of Energy Efficiency in Manufacturing Industries for Climate and Competitiveness

The manufacturing industry in Europe is currently enfacing one of its greatest challenges due to the emission reductions needed to reach carbon neutrality by the middle of this century. The European Union’s Energy Efficiency Directive and Green Deal will force manufacturing industries to significantly reduce their present energy consumption, but at the same time sustain their competitiveness globally. Here we use the Latvian manufacturing industry as a case to analyse how different macro-level factors have affected its energy use and how the industrial energy efficiency has progressed during the last decade. We apply the Log-Mean Divisia index decomposition method to decompose the energy use in the manufacturing subsectors over the period of the past ten years from 2010 to 2019. The findings unravel the key driving factors of industrial energy consumption, which could serve as a valuable basis for effective energy efficiency policymaking in the future. The results show that energy consumption trends differed across industrial subsectors and the effect of industrial energy efficiency improvements was more pronounced in the period following the entry into force of Energy Efficiency Law in Latvia. Significant increases in energy consumption are observed in the two largest Latvian manufacturing subsectors, such as the non-metallic minerals production sector and the wood processing sector, where the current pace of energy efficiency improvements cannot compensate for the effect of increasing industrial activity, which increases overall industrial energy consumption. The results suggest that the Latvian manufacturing industry is at the crossroads of the sustainability dilemma between economic gains and energy saving targets.