Evaluation of rope shovel operators in surface coal mining using a Multi-Attribute Decision-Making model

Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making （MADM） model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alter- natives. Shovel operators are considered the alternatives, The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective opti- mization. Multi-objective optimization was conducted with the overall goal of minimizing energy con- sumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process （AHP）, The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE If. The research results presented here may be used by mining professionals to held evaluate the performance of rode shovel operators in surface mining.

Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin

The water quality and energy consumption of wastewater treatment plants(WWTPs)in Taihu Basin were evaluated on the basis of the operation data from 204 municipal WWTPs in the basin by using various statistical methods.The influent ammonia nitrogen(NH3-N)and total nitrogen(TN)of WWTPs in Taihu Basin showed normal distribution,whereas chemical oxygen demand(COD),biochemical oxygen demand(BOD5),suspended solid(SS),and total phosphorus(TP)showed positively skewed distribution.The influent BOD5/COD was 0.4%-0.6%,only 39.2%SS/BOD5 exceeded the standard by 36.3%,the average BOD5/TN was 3.82,and the probability of influent BOD5/TP>20 was 82.8%.The average energy consumption of WWTPs in Taihu Basin in 2017 was 0.458 kWh/m^3.The specific energy consumption of WWTPs with a daily treatment capacity of more than 5×10^4 m^3 in Taihu Basin was stable at 0.33 kWh/m^3.A power function relationship was observed between the reduction in COD and NH3-N and the specific energy consumption of pollutant reduction,and the higher the pollutant reduction is,the lower the specific energy consumption of pollutant reduction presents.In addition,a linear relationship existed between the energy consumption of WWTPs and the specific energy consumption of influent volume and pollutant reduction.Therefore,upgrading and operation with less energy consumption of WWTPs is imperative and the suggestions for Taihu WWTPs based on stringent discharge standard are proposed in detail.

Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.