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To further elucidate the variations of secondary oxidation spontaneous combustion risk of lignite under different air flows and immersion time. Secondary oxidation experiments of short-term water-immersed coal and long-term water-immersed coal were conducted under four air flows. The results show that, the presence of a temperature inflection point during primary oxidation process, when coal temperature exceeds it, both the oxygen consumption rate and heat release intensity of long-term water-immersed coal are lower, furthermore, decrease in air flow leads to reduction in the temperature inflection point. The oxygen consumption rate and heat release intensity during the primary oxidation process exceed those observed during the subsequent secondary oxidation process. In the secondary oxidation process, long-term water-immersed coal exhibits higher rates of oxygen consumption and heat release intensity compared to short-term water-immersed coal. Additionally, the oxygen-consuming activation energy for oxygen consumption of long-term water-immersed coal is lower. The increase in air flow and water immersion time generally leads to the extreme value of the limit parameters, such as the local maximum of minimal thickness of residual coal and the lower limit oxygen fraction, the local minimum of the maximal air leakage intensity develops in the direction of increasing the risk of spontaneous combustion of coal in goaf.

This study evaluated the seasonal variability of water quality in the Tilacancha River, the water source that supplies Chachapoyas, and the rural communities of Levanto and San Isidro del Maino of Perú. Eighteen physical, chemical, and microbiological water parameters were evaluated at five sampling points in two seasons (rainy and dry). To determine water quality, the results obtained for the parameters evaluated were compared with the Maximum Permissible Limits (MPL) established in the Regulation on Water Quality for Human Consumption (DS Nº 031-2010-SA), approved by the Environmental Health Directorate of the Ministry of Health. In addition, a Pearson correlation was performed to estimate the correlation between the variables evaluated. The results showed that microbiological parameters exceeded the MPLs in both periods evaluated, such as the case of total coliforms (44 MPN.100 mL-1), fecal coliforms (25 MPN.100 mL-1), and E. coli (5.45 MPN.100 mL-1), these microbiological parameters reported a positive correlation with turbidity, temperature, total dissolved solids, and flow rate. In addition, aluminum (Al) and manganese (Mn) exceeded the MPL in the rainy (0.26 mg Al.L-1) and dry (1.41 mg.Mn-1.L-1) seasons, respectively. The results indicated that the water of the Tilacancha River is not suitable for human consumption. Therefore, it must be treated in drinking water treatment plants to be used as drinking water.

The coal spontaneous combustion (CSC) in the goaf of a coal mine poses a significant safety hazard. This study investigates the gas production characteristics, apparent activation energy, and limiting parameters of coal samples with varying particle sizes during the low-temperature oxidation stage under both primary and secondary oxidation conditions through a temperature-programmed experiment. The results indicate that smaller particle size leads to higher rates of O 2 consumption, CO generation, and heat release intensity in coal samples; The O 2 consumption rate, CO generation rate, and heat release intensity during the secondary oxidation of coal samples are relatively high in the initial stage of the experiment. However, in the later stage of the experiment, there is a reversal in these parameters; the selection of the most appropriate model from the 22 commonly observed reaction kinetics mechanism functions revealed that the apparent activation energy of the coal sample decreases during secondary oxidation compared to primary oxidation within the temperature range of 80℃ to 130℃. However, a reversal occurs between temperatures of 140℃ and 170℃, indicating that secondary oxidation initially enhances the low-temperature oxidation characteristics but weakens them in later stages; The various particle sizes under both primary and secondary oxidation conditions significantly influence the limit parameters of CSC, with secondary oxidation being more prone to inducing SC of coal in goaf compared to primary oxidation.

The limit parameters of coal spontaneous combustion are important indicators for determining the risk of spontaneous combustion in coal seams. By analyzing the limit parameters of coal spontaneous combustion, the dangerous areas of coal spontaneous combustion can be determined, and corresponding measures can be taken to avoid the occurrence of fires. In order to accurately predict the limit parameters of coal spontaneous combustion, the prediction model of coal spontaneous combustion limit parameters based on GA-SVM was constructed by coupling genetic algorithm (GA) and support vector machine (SVM). Meanwhile, the GA and particle swarm optimization algorithm (PSO) were used to optimize the back propagation neural network (BPNN) to construct the GA-BPNN and PSO-BPNN prediction models, respectively. To predict the intensity of air leakage of the upper limit of coal spontaneous combustion in the goaf, the prediction results of the models were compared and analyzed using MAE, MAPE, RMSE, and R2 as the prediction performance evaluation indexes. The results show that the MAE of the GA-SVM model, the PSO-BPNN model, and the GA-BPNN model are 0.0960, 0.1086, and 0.1309, respectively; the MAPE is 2.46%, 3.11%, and 3.69%, respectively; the RMSE is 0.1180, 0.1789, and 0.2212, respectively; and the R2 is 0.9921, 0.9818, and 0.9722. The prediction results of the GA-SVM model are the most optimal in four evaluation indexes, followed by the PSO-BPNN and the GA-BPNN models. Applying each model to the prediction of minimum residual coal thickness in the goaf of a coal mine in Shanxi, the GA-SVM model has higher accuracy, which further verifies the universality and stability of the model and its suitability for the prediction of coal spontaneous combustion limit parameters.

In this paper, a Joint set - up procedure for tuning metaheuristic algorithms’ parameters is proposed. The approach is applied to a genetic algorithm (GA) and tested further on the artificial bee colony (ABC) algorithm. The joint influence of parameters (the crossover and mutation probabilities for GA and the number of population and limit for ABC) on the performance of the algorithms is investigated. As a case study, a model parameter identification of an E. coli fed-batch cultivation process is considered. E. coli is one of the most commonly used bacteria for producing medical substances in the pharmaceutical industry. The development of an effective model of a fed-batch cultivation process is very important. The processes in a bioreactor are usually described by a system of parametric nonlinear differential equations. The model parameter identification is a difficult optimization problem, which cannot be solved by applying traditional numerical methods. Feasibilities of GA and ABC for a model parameter identification of a nonlinear fed-batch cultivation process based on real experimental data are presented. The application of the proposed Joint set - up approach leads to a significant improvement in the performance of GA and ABC. As a result, a reasonable enhancement of the E. coli cultivation model accuracy is achieved. The main advantage of the tuning procedure, which searches an optimal set of values of GA and ABC control parameters, focusing on promising intervals of variation of the parameter values and refining their ranges, is that the computational efforts are reduced by more than 60% for the ABC algorithm and more than 90% for GA.

Better terrain accessibility of military vehicle makes it possible to project force at desired points in a theatre of operation. The factors responsible for terrain accessibility are slope, obstacles and soil. Torque requirement for meeting vehicle speed and gradient requirement is understood and can be analytically arrived at. It can be met by appropriate choice of engine and transmission using. There is dearth of information as well as a common metric in quantification of terrain accessibility especially soft soil trafficability. Approach adopted in this study is that of characterisation of vehicle in terms of mobility characteristic and mobility limit parameters and comparing them with vehicle in-service worldwide. Simple empirical relation has been preferred over complex analytical approach for mobility prediction and gradient climbing capability in sand has been predicted and compared with other vehicles. parametric study for tyre sizes vis-a-vis mobility parameters have been obtained and results have been presented for chosen vehicle configuration. Second part of this study is obstacle crossing capability study for standard set of obstacles. Vehicle model has been built in multi-body environment and parameters of significance viz., wheel displacement to verify correctness of model and acceleration at CG for ride comfort and ground reactions for evaluation of dynamic loads on axles have been obtained. Vehicle drivetrain configuration to achieve desired terrain accessibility in terms of soft-soil trafficability and obstacle crossing has been demonstrated.

The unimolecular alkene elimination reaction class of fatty acid alkyl esters (FAAEs) is a crucial component in the low-temperature combustion mechanism for biodiesel fuels. However, thermo-kinetic parameters for this reaction class are scarce, particularly for the large-size molecules over four carbon atoms and intricate branched-chain configurations. Thermo-kinetic parameters are essential for constructing a reaction mechanism, which can be used to clarify the chemical nature of combustion for biodiesel fuels. In this paper, the B3LYP method, in conjunction with the 6-311G(d,p) basis set, is used to carry out geometry optimization of the species participating in the reactions. Frequency calculations are further executed at the same level of theory. Additionally, coupled with the 6-311G(d,p) basis set, the B3LYP method acts as the low-level ab initio approach, while the Gaussian-4 (G4) composite method serves as the high-level ab initio approach within the isodesmic reaction correction scheme. The CCSD(T) approach is employed to verify the consistency of the electronic energy ascertained through the G4 method. The isodesmic reaction method (IRM) is used to obtain the energy barriers and reaction enthalpies for unimolecular alkene elimination reaction class of FAAEs. Based on the reaction class transition state theory (RC-TST), high-pressure-limit rate coefficients were computed, with asymmetric Eckart tunneling corrections applied across 500~2000 K temperature range. Rate rules at the high-pressure-limit are obtained through the averaging of rate coefficients from a representative collection of reactions, which incorporate substituent groups and carbon chains with different sizes and lengths. Ultimately, the energy barriers, reaction enthalpies, and rate rules at the high-pressure-limit and kinetic parameters expressed as (A, n, E) are supplied for developing the low-temperature combustion mechanism of biodiesel fuels.

This paper studies the computerized tooth profile generation of involute gears cut with rack-type cutters. Based on the theory of gearing the mathematical models of generating cutter with asymmetric involute teeth and generated involute gears are given. Beveloid (conical involute) gears are considered for a generalized type of involute gearing for connecting parallel shafts. Effective limits of involute design parameters that determine the actual tip circle radii of the generated gears are investigated. An approach based on contact path of mating gears is proposed to eliminate further operations for standard value of tip circle radius. Computer simulation programs are developed to obtain graphs of generating tools and generated teeth surfaces.

A limit formula from q-Racah polynomials to big q-Jacobi polynomials is given which can be considered as a limit formula for orthogonal polynomials. This is extended to a multi-parameter limit with 3 parameters, also involving (q-)Hahn polynomials, little q-Jacobi polynomials and Jacobi polynomials. Also the limits from Askey-Wilson to Wilson polynomials and from q-Racah to Racah polynomials are given in a more conceptual way.

We show that several machine learning estimators, including square-root least absolute shrinkage and selection and regularized logistic regression, can be represented as solutions to distributionally robust optimization problems. The associated uncertainty regions are based on suitably defined Wasserstein distances. Hence, our representations allow us to view regularization as a result of introducing an artificial adversary that perturbs the empirical distribution to account for out-of-sample effects in loss estimation. In addition, we introduce RWPI (robust Wasserstein profile inference), a novel inference methodology which extends the use of methods inspired by empirical likelihood to the setting of optimal transport costs (of which Wasserstein distances are a particular case). We use RWPI to show how to optimally select the size of uncertainty regions, and as a consequence we are able to choose regularization parameters for these machine learning estimators without the use of cross validation. Numerical experiments are also given to validate our theoretical findings.