Estimation of diameter and surface area flux of bubbles based on operational gas dispersion parameters by using regression and ANFIS

Adaptive neuro fuzzy inference system (ANFIS) procedure and regression methods were used to predict the Sauter mean bubble (bubble diameter) and surface area flux of the bubble in a flotation process. The operational conditions of flotation, impeller peripheral speed, superficial gas velocity, and weight percent solids were used as inputs of methods. By using the mentioned operational conditions, the non linear regression results showed that Sauter mean, and surface area flux of the bubble are predictable variables, where the coefficients of determination (R 2 ) are 0.57 and 0.74, respectively. To increase the accuracy of prediction an ANFIS model with cluster radius of 0.4 was applied. ANFIS model was capable of estimating both Sauter mean, and surface area flux of the bubble, where in a testing stage, satisfactory correlations, R 2 = 0.78, and 0.86, were achieved for Sauter mean, and surface area flux of bubble, respectively. Results show that the proposed ANFIS model can accurately estimate outputs and be used in order to predict the parameters without having to conduct the new experiments in a laboratory.

Integrating CART Algorithm and Multi-source Remote Sensing Data to Estimate Sub-pixel Impervious Surface Coverage:A Case Study from Beijing Municipality,China

The sub-pixel impervious surface percentage(SPIS) is the fraction of impervious surface area in one pixel,and it is an important indicator of urbanization.Using remote sensing data,the spatial distribution of SPIS values over large areas can be extracted,and these data are significant for studies of urban climate,environment and hydrology.To develop a stabilized,multi-temporal SPIS estimation method suitable for typical temperate semi-arid climate zones with distinct seasons,an optimal model for estimating SPIS values within Beijing Municipality was built that is based on the classification and regression tree(CART) algorithm.First,models with different input variables for SPIS estimation were built by integrating multi-source remote sensing data with other auxiliary data.The optimal model was selected through the analysis and comparison of the assessed accuracy of these models.Subsequently,multi-temporal SPIS mapping was carried out based on the optimal model.The results are as follows:1) multi-seasonal images and nighttime light(NTL) data are the optimal input variables for SPIS estimation within Beijing Municipality,where the intra-annual variability in vegetation is distinct.The different spectral characteristics in the cultivated land caused by the different farming characteristics and vegetation phenology can be detected by the multi-seasonal images effectively.NLT data can effectively reduce the misestimation caused by the spectral similarity between bare land and impervious surfaces.After testing,the SPIS modeling correlation coefficient(r) is approximately 0.86,the average error(AE) is approximately 12.8%,and the relative error(RE) is approximately 0.39.2) The SPIS results have been divided into areas with high-density impervious cover(70%–100%),medium-density impervious cover(40%–70%),low-density impervious cover(10%–40%) and natural cover(0%–10%).The SPIS model performed better in estimating values for high-density urban areas than other categories.3) Multi-temporal SPIS mapping(1991–2016) was conducted based on the optimized SPIS results for 2005.After testing,AE ranges from 12.7% to 15.2%,RE ranges from 0.39 to 0.46,and r ranges from 0.81 to 0.86.It is demonstrated that the proposed approach for estimating sub-pixel level impervious surface by integrating the CART algorithm and multi-source remote sensing data is feasible and suitable for multi-temporal SPIS mapping of areas with distinct intra-annual variability in vegetation.

Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining

The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.

Co-inhibition of methanogens for methane mitigation in biodegradable wastes

The inhibition effects and mechanisms of chlorinated methane and acetylene on methanogenesis in the anaerobic digestion process of the biodegradable wastes were investigated.It was found that both chloroform and acetylene could effectively inhibit methanogens while the biodegradability of the wastes was not affected.Acetylene inhibited the activity of methanogens,while chloroform inhibited metabolic process of methanogenesis.A central composite design（CCD） and response surface regression analysis（RSREG） were employed to determine the optimum conditions and interaction effects of chloroform and acetylene in terms of inhibition effciency,production of volatile fatty acids（VAF） and molar ratio of propionic acid to acetic acid.Chloroform had significant effect on enhancing the production of VFA（F = 121.3;p 〈 0.01）,and acetylene promoted the inhibition effciency（F = 99.15;p 〈 0.05） more effectively than chloroform（F = 9.72;p 〉 0.05）.In addition,a maximum molar ratio of propionic acid to acetic acid of 1.208 was estimated under the optimum conditions of chloroform concentration of 9.05 mg/kg and acetylene concentration of 3.6×10^-3（V/V）.Hence,methanogens in the wastes can be inhibited while the stabilization process of the biodegradable wastes can still work well,as propionic acid generated during the inhibition process could hardly be utilized by methanogens.

Optimization of vinegar-steaming process for Wuweizi (Fructus Schisandrae Chinensis) with response surface method

OBJECTIVE:To optimize the vinegar-steaming process of Wuweizi(Fructus Schisandrae Chinensis)using the response surface method(RSM)based on the Box-Behnken design.METHODS:A regression model was constructed with the response variables,the content of Deoxyschizandrin,and the three explanatory factors:length of steaming time,the quantity of vinegar and length of moistening time to evaluate the effects on the processing of Wuweizi(Fructus SchisandraeChinensis).RESULTS:There was a linear relationship between the content of Deoxyschizandrin and the three explanatory factors.When the steaming time was5.49 h,with 2.365 g of vinegar added and a moistening time of 4.13 h,the content of Deoxyschizandrin reached the maximum predicted value of0.1076%,and under the conditions the average content of Deoxyschizandrin was 0.1058%.CONCLUSION:The correlation coefficient of thenonlinear mathematical model was relatively high and the model matched the data well,potentially providing a method for the study of the steaming process.

Engine universal characteristic modeling based on improved ant colony optimization

There have been some mathematics methods to model farm vehicle engine universal characteristic mapping(EUCM).Nevertheless,any of different mathematics methods used would possess its own strengths and weaknesses.As a result,these modeling methods about EUCM are not the same among the most vehicle manufacturers.In order to obtain a better robustness EUCM,an improved ant colony optimization was introduced into a traditional cubic surface regression method for modeling EUCM.Based on this method,the test data were regressed into a three-dimensional cubic surface,after that it was cut by some equal specific fuel consumption(ESFC)planes,more than twenty two-dimensional ESFC equations were obtained.Furthermore,the engine speed in every ESFC equation was discretized to obtain a set of ESFC points,and this set of ESFC points was linked into a closed curve by a given sequence via the improved ant colony algorithm.In order to improve the modeling speed,dimensionality reduction and discretization methods were adopted.In addition,a corresponding simulation platform was also developed to obtain an optimal system configuration.There were 48000 simulation search tests carried out on the platform,and the major parameters of the algorithm were determined.In this way the EUCM was established successfully.In contrast with other methods,as a result of the application of the novel bionic intelligent algorithm,it has better robustness,less distortion and higher calculating speed,and it is available for both gasoline engines and diesel engines.