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Accurate perception of the key stratum instability can improve the safety of coal mining and also provide a basis for alleviating overlying rock strata destruction and environmental disturbance. To efficiently evaluate the instability of the key stratum and its threat to safe mining and environmental protection, the fracture characteristics and weakening mechanisms were studied through physical simulation, theoretical analysis, and field measurement. A scheme and the parameters of confined blasting in water-filled deep hole presplit technology (CBWDHPT) for thick and hard roof (THR) weakening were proposed. Research studies showed that, after the THR fractured into large blocks, the subsequent sliding instability induced serious support-crushing accidents; however, increasing the support strength could only provide limited control. Confined water and infiltrated modified rock mass functioned as the transfer load medium of the explosives, and the CBWDHPT fully utilized high explosion energy to break rocks. Consequently, the collapse and filling of the immediate roof and low-positioned THR, as well as the timely cutting off the middle-positioned THR, could be realized, which alleviated the migration space of THR blocks, overlying strata destruction, and earth-surface step subsidence. Finally, the environmentally friendly strategy (including the CBWDHPT and hydraulic support optimization) for overlying rock strata protection was proposed. In the industrial test, the THR was broken into blocks of different sizes after utilizing the CBWDHPT, and the support working resistance was significantly decreased. It was concluded that the environmentally friendly strategy could effectively reduce the risk of overlying rock strata destruction. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling of the biogas production rate from a real full-scale sludge digestion process. In the computational analysis, five process-related parameters such as influent sludge flow rate, total solids content, total volatile solids content, alkalinity, and volatile fatty acids concentration were considered as the input variables for the proposed models. In the ANN-based modeling, a benchmark comparison of 11 backpropagation (BP) algorithms was employed in the first step, and the scaled conjugate gradient algorithm was chosen as the best BP algorithm in terms of their respective mean squared errors. According to the selected BP algorithm (scaled conjugate gradient BP), the number of neurons at the hidden layer was optimized as 14, and the coefficient of determination (R2) was obtained as 0.65 for the optimal three-layer ANN structure (5:14:1). In the second part of the study, a MISO (multiple-input single-output)-type fuzzy-logic model was developed, and five input variables were fuzzified in a knowledge-based manner. For the fuzzy subsets, trapezoidal membership functions were implemented with 10 and 20 levels, and a Mamdani-type fuzzy inference system (FIS) was used to employ 394 rules in the if–then form. The product, summation, and centroid methods were employed, respectively, for implication, aggregation, and defuzzification processes conducted in the FIS. Fuzzy logic-produced forecasts were compared with the estimations obtained from both ANN-based model and a polynomial multiple regression-based model (employed as the third part of this study) derived in this study. Findings of this study clearly indicated that compared to ANN model and conventional multiple regression approach, the proposed MISO fuzzy logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting biogas production rate from a full-scale treatment plant with a satisfactory R2 value of 0.88. [ABSTRACT FROM AUTHOR]

Based on the occurrence conditions of a thick and hard main roof and wedge-structure immediate roof in the Zhuxianzhuang Coal Mine, the fracture characteristics and instability migration law of a thick and hard roof (THR) were examined via physical simulations. Mining zones were divided with respect to the strata behaviors and roof control difficulty levels, and the principles and methods of zonal control under THR were put forward. This study proposed a coordinated control strategy of using confined blasting in water-filled deep holes, and reasonable support optimization, which could effectively reduce the roof fracture size, increases the supporting intensity and eliminate roof-control disasters. The length of confined blasting blocks and supporting intensity were calculated using a mechanical model for roof control in the strong strata behavior zone and less-strong strata behavior zone. These key parameters were determined as 20–25 m and 1.15–1.28 MPa, respectively, and the mining strategy was successfully applied in working face 880, performing high security and reasonable economical efficiency. [ABSTRACT FROM AUTHOR]

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A hybrid programming methodology was proposed to derive a simple empirical formulation for the estimation of the required pipe diameter in the sizing problems (Type 3) of pipe distribution systems. The model was derived based on multiple regression-based analysis using the Richardson's extrapolation approach and the Levenberg–Marquardt algorithm with double precision. The proposed formulation was developed using a total of 300,000 different data points within the framework of MATLAB and DataFit scientific software. The application of the model was explored for a wide range of five fundamental pipeline design variables [absolute roughness of the pipe wall (ε=0–9 mm), water temperature (T=5°C–100°C), pipe length (L=5–500 m), flow rate (Q=0.01–1 m3/s), and head loss (Δh=1–30 m)] and tested against a total of 10,000 additional computational scenarios and the available models reported in the literature. The uncertainty prediction of the proposed formula was quantified and compared with those of existing prediction models. For the new empirical equation, the mean prediction errors between the estimated and the theoretical diameter values (calculated from the numerical solution of the Colebrook–White equation) were significantly smaller than those of existing models. Moreover, the narrowest uncertainty bands, the lowest 95% confidence prediction error intervals, and the lowest amounts of expanded uncertainty (U95) were achieved for the proposed model. Other statistics (e.g., mean absolute relative error, absolute relative error, coefficient of variation of root mean squared error, determination coefficient) also corroborated that the proposed empirical model produced realistic estimations that were superior to those obtained from other well-known explicit models in the literature. The findings of this study concluded that the computational analysis yielded a simple mathematical structure to be easily and accurately used for educational and practical purposes. [ABSTRACT FROM AUTHOR]

This study aimed to perform a comparative analysis of the performance of five models (Gompertz, logistic, Richards, the first-order, artificial neural networks) in predicting methane production rate from anaerobic digestion of livestock manures. The input variables were fermentation time, digestion temperature, biogas temperature, ambient temperature, pH, and specific biogas production rate. The physicochemical compositions of cow manure and sheep manure showed that volatile solid (VS) contents were close to each other in manure compositions (77.6% and 64.7%, respectively), while the potential of methane production from cow manure (673.44 mL CH4/g VS) was greater than that from sheep manure (320.32 mL CH4/g VS). The determination coefficients (R2) for logistic function, Gompertz, Richards, the first-order, and ANN models were obtained as 0.968, 0.967, 0.975, 0.825, and 0.995 for the cow manure, respectively. In case of the sheep manure, the R2 values obtained from these models were 0.976, 0.979, 0.981, 0.968 and 0.991, respectively. Although the determination coefficients of all models were in satisfactory agreement with the experimental data, the ANN model showed competitive lower RMSE values of 0.111 and 0.164 for cow and sheep manure data sets, respectively, indicating its superior performance than other models. [ABSTRACT FROM AUTHOR]

A prognostic approach based on a MISO (multiple inputs and single output) fuzzy logic model was introduced to estimate the pressure difference across a gas turbine (GT) filter house in a heavy-duty power generation system. For modelling and simulation of clogging of the GT filter house, nine real-time process variables (ambient temperature, humidity, ambient pressure, GT produced load, inlet guide vane position, airflow rate, wind speed, wind direction and PM10 dust concentration) were fuzzified using a graphical user interface within the framework of an artificial intelligence-based methodology. The results revealed that the proposed fuzzy logic model produced very small deviations and showed a superior predictive performance than the conventional multiple regression methodology, with a very high determination coefficient of 0.974. A complicated dynamic process, such as clogging phenomenonin heavy-duty GT system, was successfully modelled due to high capability of the fuzzy logic-based prognostic approach in capturing the nonlinear interactions. [ABSTRACT FROM AUTHOR]

The discussers mentioned that "the developed explicit solution of Eq. (7) of the original paper is applicable only to the transitional region and cannot predict the pipe diameter for either the rough ( HT ht ) or the smooth ( HT ht ) flow regime because for these values, Eq. (7) yields HT ht ." These ranges correspond an absolute roughness of the pipe wall ( HT ht ) range of 0.000396-8.999052 mm. [Extracted from the article]