Cooperative prediction method of gas emission from mining face based on feature selection and machine learning

Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientifc and accurate prediction of gas emission quantity in the mining face.The collaborative prediction model was screened by precision evaluation index.Samples were pretreated by data standardization,and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods.A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations.Determination coefcient,normalized mean square error,mean absolute percentage error range,Hill coefcient,mean absolute error,and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model.As such,the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out,and seven optimized collaborative forecasting models were fnally determined.Results show that the judgement coefcients,normalized mean square error,mean absolute percentage error,and Hill inequality coefcient of the 7 optimized collaborative prediction models are 0.969–0.999,0.001–0.050,0.004–0.057,and 0.002–0.037,respectively.The determination coefcient of the fnal prediction sequence,the normalized mean square error,the mean absolute percentage error,the Hill inequality coefcient,the absolute error,and the mean relative error are 0.998%,0.003%,0.022%,0.010%,0.080%,and 2.200%,respectively.The multi-parameter,multi-algorithm,multi-combination,and multijudgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity.

Investigating fatigue behavior of gear components with the acoustic emission technique

A novel method is presented to evaluate the complicated fatigue behavior of gears made of20Cr2Ni4 A.Fatigue tests are conducted in a high-frequency push-pull fatigue tester,and acoustic emission（AE）technique is used to acquire metal fatigue signals.After analyzing large number of AE frequency spectrum,we find that：the crack extension can be expressed as the energy of specific frequency band,which is abbreviated as F-energy.To further validate the fatigue behavior,some correlation analysis is applied between F-energy and some AE parameters.Experimental results show that there is significant correlation among the Fenergy,root mean square（RMS）,relative energy,and hits.The findings can be used to validate the effectiveness of the F-energy in predicting fatigue crack propagation and remaining life for parts in-service.F-energy,as a new AE parameter,is first put forward in the area of fatigue crack growth.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Characterization and prediction of tailpipe ammonia emissions from in-use China 5/6 light-duty gasoline vehicles

On-road tailpipe ammonia (NH3) emissions contribute to urban secondary organic aerosol formation and have direct or indirect adverse impacts on the environment and human health. To understand the tailpipe NH3 emission characteristics, we performed comprehensive chassis dynamometer measurements of NH3 emission from two China 5 and two China 6 light-duty gasoline vehicles (LDGVs) equipped with three-way catalytic converters (TWCs). The results showed that the distance-based emission factors (EFs) were 12.72 ± 2.68 and 3.18 ± 1.37 mg/km for China 5 and China 6 LDGVs, respectively. Upgrades in emission standards were associated with a reduction in tailpipe NH3 emission. In addition, high NH3 EFs were observed during the engine warm-up period in cold-start cases owing to the intensive emissions of incomplete combustion products and suitable catalytic temperature in the TWCs. Notably, based on the instantaneous NH3 emission rate, distinct NH3–emitting events were detected under high/extra high velocity or rapid acceleration. Furthermore, NH3 emission rates correlated well with engine speed, vehicle specific power, and modified combustion efficiency, which were more easily accessible. These strong correlations were applied to reproduce NH3 emissions from China 5/6 LDGVs. The predicted NH3 EFs under different dynamometer and real-world cycles agreed well with existing measurement and prediction results, revealing that the NH3 EFs of LDGVs in urban routes were within 8.55–11.62 mg/km. The results presented here substantially contribute to improving the NH3 emission inventory for LDGVs and predicting on-road NH3 emissions in China.

Multi-process production occurs in the iron and steel industry,supporting‘dual carbon'target:An in-depth study of CO_(2)emissions from different processes

Reducing CO_(2)emissions of the iron and steel industry,a typical heavy CO_(2)-emitting sector is the only way that must be passed to achieve the‘dual-carbon’goal,especially in China.In previous studies,however,it is still unknown what is the difference between blast furnace basic oxygen furnace(BF-BOF),scrap-electric furnace(scrap-EF)and hydrogen metallurgy process.The quantitative research on the key factors affecting CO_(2)emissions is insufficient There is also a lack of research on the prediction of CO_(2)emissions by adjusting industria structure.Based on material flow analysis,this study establishes carbon flow diagrams o three processes,and then analyze the key factors affecting CO_(2)emissions.CO_(2)emissions of the iron and steel industry in the future is predicted by adjusting industrial structure The results show that:(1)The CO_(2)emissions of BF-BOF,scrap-EF and hydrogen metallurgy process in a site are 1417.26,542.93 and 1166.52 kg,respectively.(2)By increasing pellet ratio in blast furnace,scrap ratio in electric furnace,etc.,can effectively reduce CO_(2)emissions(3)Reducing the crude steel output is the most effective CO_(2)reduction measure.There is still 5.15×10^(8)-6.17×10^(8) tons of CO_(2)that needs to be reduced by additional measures.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.