Intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization

The decision-making method of tunnel boring machine(TBM)operating parameters has a significant guiding significance for TBM safe and efficient construction,and it has been one of the TBM tunneling research hotspots.For this purpose,this paper introduces an intelligent decision-making method of TBM operating parameters based on multiple constraints and objective optimization.First,linear cutting tests and numerical simulations are used to investigate the physical rules between different cutting parameters(penetration,cutter spacing,etc.)and rock compressive strength.Second,a dual-driven mapping of rock parameters and TBM operating parameters based on data mining and physical rules of rock breaking is established with high accuracy by combining rock-breaking rules and deep neural networks(DNNs).The decision-making method is established by dual-driven mapping,using the effective rock-breaking capacity and the rated value of mechanical parameters as constraints and the total excavation cost as the optimization objective.The best operational parameters can be obtained by searching for the revolutions per minute and penetration that correspond to the extremum of the constrained objective function.The practicability and effectiveness of the developed decision-making model is verified in the SecondWater Source Channel of Hangzhou,China,resulting in the average penetration rate increasing by 11.3%and the total cost decreasing by 10%.

Optimization of Control Loops and Operating Parameters for Three-Phase Separators Used in Oilfield Central Processing Facilities

In this study,the Stokes formula is used to analyze the separation effect of three-phase separators used in a Oilfield Central Processing Facility.The considered main influencing factors include(but are not limited to)the typical size of oil and water droplets,the residence time and temperature of fluid and the dosage of demulsifier.Using the“Specification for Oil and Gas Separators”as a basis,the control loops and operating parameters of each separator are optimized Considering the Halfaya Oilfield as a testbed,it is shown that the proposed approach can lead to good results in the production stage.

Effects of operating parameters on the flow field in slab continuous casting molds with narrow widths

Computational simulations and high-temperature measurements of velocities near the surface of a mold were carried out by using the rod deflection method to study the effects of various operating parameters on the flow field in slab continuous casting(CC)molds with narrow widths for the production of automobile exposed panels.Reasonable agreement between the calculated results and measured subsurface velocities of liquid steel was obtained under different operating parameters of the CC process.The simulation results reveal that the flow field in the horizontal plane located 50 mm from the meniscus can be used as the characteristic flow field to optimize the flow field of molten steel in the mold.Increases in casting speed can increase the subsurface velocity of molten steel and shift the position of the vortex core downward in the downward circulation zone.The flow field of liquid steel in a 1040 mm-wide slab CC mold can be improved by an Ar gas flow rate of 7 L·min^−1 and casting speed of 1.7 m·min^−1.Under the present experimental conditions,the double-roll flow pattern is generally stable at a submerged entry nozzle immersion depth of 170 mm.

Effects of structural and operating parameters of ECP fan on dust particles removed in the transition flow regime

An enclosed cyclone passageway(ECP)dust-collecting fan is discussed.The ECP fan separates dust by centrifugal force originating from a driven spiral airflow,and its design takes the constraints of Chinese underground coal mines into consideration.Using the force equilibrium law,a general equation for dust removal in the centrifugal dust removal section(CDRS)of the ECP fan is deduced.This general equation is simplified using the CDRS structure and the fan operating parameters and is analysed numerically.The attractive results show that increases in the airflow rate of the fan,the structural ratio of the ECPs and the radius of the extended axis can improve the dust removal performance of the CDRS.Furthermore,the effects of the structural ratio and the radius on dust removal dominate over that of the flow rate,and the effect of the structural ratio is more significant than that of the radius.

Effects of operating parameters on oxidative coupling of methane over Na-W-Mn/SiO_2 catalyst at elevated pressures

The effects of operating parameters on oxidative coupling of methane （OCM） over Na-W-Mn/SiO2 catalyst have been studied at elevated pressures of 0.2, 0.3 and 0.4 MPa under low gaseous hourly space velocity （GHSV） and low temperature conditions. Experimental results show that when the operating pressure is increased, C2＋ yield slightly decreases, while the maximum ratio of ethylene to ethane remains unchanged. Moreover, it has been found empirically that increase of pressure does not affect the catalyst behavior permanently, the catalyst recovers its original low pressure performance without hysteresis behavior by reducing the pressure. Under the investigated conditions, when oxygen is completely consumed, the increase of GHSV leads to improvement in C2 selectivity, while C3＋ and COx selectivities decrease slightly. The C2＋ selectivity increases by increase of nitrogen diluent in the feed, but the C3＋ hydrocarbons selectivities decrease with increase of nitrogen since it is possible that further dilution at high pressure may reduce the probability of collision between CH3 and C2＋ hydrocarbons. During the stability test at high pressure, the catalyst performance remains unchanged throughout the 20 h running. The fresh and used catalysts were characterized using XRD, SEM and N2 adsorption-desorption methods. It was found that the phase transformation of the support from α-cristobalite to tridymite and quartz does not have obvious effect on catalyst performance at high pressure.

Study on the operating parameters of the 10 kW SOFC-CHP system with syngas

Solid oxide fuel cell combined with heat and power(SOFC-CHP)system is a distributed power generation system with low pollution and high efficiency.In this paper,a 10 kW SOFC-CHP system model using syngas was built in Aspen plus.Key operating parameters,such as steam to fuel ratio,stack temperature,reformer temperature,air flow rate,and air preheating temperature,were analyzed.Optimization was conducted based on the simulation results.Results suggest that higher steam to fuel ratio is beneficial to the electrical efficiency,but it might decrease the gross system efficiency.Higher stack and reformer temperatures contribute to the electrical efficiency,and the optimal operating temperatures of stack and reformer when considering the stack degradation are 750℃and 700℃,respectively.The air preheating temperature barely affects the electrical efficiency but affects the thermal efficiency and the gross system efficiency,the recommended value is around 600℃under the reference condition.

Efect of operating parameters on sulfide biotransformation to sulfur

A laboratory-scale bioreactor with polyethylene semi-soft packing was constructed and utilized to determine the efficlency of sulfide biotransformation to sulfur under various operating parameters. Sodium sulfide dissolved in tap water was pumped into the bioreactor as sulfide for biological desulfurization. The sulfide, sulfur and sulfate-S in the effluent and the sulfide purged as gas-phase HzS were determined to investigate the effects of operating parameters, such as pH, DO, hydraulic retention time （HRT）, temperature and salinity, on the sulfide oxidation products. The activity of bacteria was highest at pH 7.8-8.2. The maximal sulfide removal load was 7.25 kg/（m^3·day）, with a 322.07 mg/L influent sulfide concentration and 4.80 mg/L DO. The increase of DO value corresponds to a decrease in the sulfur yield. The reactor had the highest sulfide removal load and sulfur yield at 2.55 mg/L DO. HRT had little effect on desulfurization efficiency when the sulfide removal load was kept constant. The most effective desulfurization temperature was 33℃. The sulfide removal load decreased from 2.85 to 0.51 kg/（m^3.day） with increasing salinity from 0.5% to 2.5% （m/m）.

Experimental study on influence of operating parameters on tar components from corn straw gasification in fluidized bed

Gasification is a promising approach for converting solid fuel sources, including renewable ones like biomass, for use. The main problem in biomass gasification is the formation of condensable tars, including polycyclic aromatic hydrocarbons (PAHs). This paper investigated the conversion of tar components during corn straw gasification. It analyzed collected tar components using a gas chromatograph-mass spectrograph (GC-MS). Experimental results indicate that, with increasing temperature from 700°C to 900°C, the concentrations of benzene, indene, phenanthrene, naphthalene, acenaphthylene, fluorene, and pyrene increased whereas those of toluene, phenol, 1-methylnaphthalene, and 2-methylnaphthalene decreased. As the equivalence ratio (ER) increased from 0.21 to 0.34, the concentrations of indene and phenanthrene increased from 0.148% and 0.087% to 0.232% and 0.223%, respectively. Further, the phenol content increased as ER increased from 0.21 to 0.26 and then decreased as the ER increased further to 0.34. Other parameters like the steam/biomass (S/B) ratio and catalyst also played a critical role in tar reduction. This paper demonstrates the conversion of some tar components and elucidates their chemical properties during gasification.

A comparative study of acidic,basic,and reactive dyes adsorption from aqueous solution onto kaolin adsorbent:Effect of operating parameters,isotherms,kinetics,and thermodynamics

Various studies were reported for the evaluation of the adsorption performance of kaolin clay using single dye types.This paper aimed to evaluate the comparative adsorption capacity of prepared adsorbents from Ethiopian kaolin for different dye types(Basic Yellow 28(BY 28),Congo Red(CR),and Reactive Red 120(RR 120)).Because different dye classes may have a significant impact on the removal efficiency by the prepared adsorbent.Moreover,we intended to investigate the interaction effect of adsorbent-sorbate in the adsorption phenomenon for the three different class dyes.The adsorbents from kaolin clay were prepared via mechanical treatment,beneficiation,and calcination(700℃).The effect of operating parameters(pH,adsorbent dose,contact time,dye concentration,and adsorption temperature)was evaluated.before and after adsorption of the adsorbents were characterized using FTIR spectroscopy.Furthermore,adsorption isotherm,kinetic models,and the thermodynamic processes in the adsorption phenomenon were computed.The percentage removal efficiency of dyes was recorded as 92.08%,88.63%,and 73.33%for BY 28,CR,and RR 120 dyes,respectively at the experimental condition:adsorbent dosage=1 g/100 mL,solution pH=9(BY 28),and pH=3(CR,and RR 120),contact time=60 min,initial dyes concentrations=20 mg/L,and temperature=30℃.The adsorption of adsorbates onto kaolin adsorbents was well fitted with pseudo-second-order kinetics and Langmuir isotherm models.The thermodynamic parameters indicate that the adsorption process is spontaneous and exothermic for all dyes.The comparative percentage removal of,with the same operational parameters and kaolin adsorbent,was recorded as BY 28>CR>RR120 resulting from their surface charge and molecular size/structure dyes properties.We confirm that the adsorption at each operational parameter and peak intensity of FTIR spectra,before and after adsorption,revealed that the different dye types have varied removal efficiency onto the prepared kaolin adsorbent.This is due to that being dominantly influenced by the electrostatic interaction and steric effects at the surface of the sorbent and sorbate characteristics.We deduced that the kaolin clay used as an adsorbent is highly dependent on the dye types and their featured characteristics.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Discussion of reasonable drilling parameters in impregnated diamond bit drilling

The impregnated diamond(ID)bit drilling is one of the main rotary drilling methods in hard rock drilling and it is widely used in mineral exploration,oil and gas exploration,mining,and construction industries.In this study,the quadratic polynomial model in ID bit drilling process was proposed as a function of controllable mechanical operating parameters,such as weight on bit(WOB)and revolutions per minute(RPM).Also,artificial neural networks(ANN)model for predicting the rate of penetration(ROP)was developed using datasets acquired during the drilling operation.The relationships among mechanical operating parameters(WOB and RPM)and ROP in ID bit drilling were analyzed using estimated quadratic polynomial model and trained ANN model.The results show that ROP has an exponential relationship with WOB,whereas ROP has linear relationship with RPM.Finally,the optimal regime of mechanical drilling parameters to achieve high ROP was confirmed using proposed model in combination with rock breaking principal.

Prediction of operational parameters effect on coal flotation using artificial neural network

Artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of collector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

Development of a Novel Feedforward Neural Network Model Based on Controllable Parameters for Predicting Effluent Total Nitrogen

The problem of effluent total nitrogen(TN)at most of the wastewater treatment plants(WWTPs)in China is important for meeting the related water quality standards,even under the condition of high energy consumption.To achieve better prediction and control of effluent TN concentration,an efficient prediction model,based on controllable operation parameters,was constructed in a sequencing batch reactor process.Compared with previous models,this model has two main characteristics:①Superficial gas velocity and anoxic time are controllable operation parameters and are selected as the main input parameters instead of dissolved oxygen to improve the model controllability,and②the model prediction accuracy is improved on the basis of a feedforward neural network(FFNN)with algorithm optimization.The results demonstrated that the FFNN model was efficiently optimized by scaled conjugate gradient,and the performance was excellent compared with other models in terms of the correlation coefficient(R).The optimized FFNN model could provide an accurate prediction of effluent TN based on influent water parameters and key control parameters.This study revealed the possible application of the optimized FFNN model for the efficient removal of pollutants and lower energy consumption at most of the WWTPs.

Soft ground tunnel lithology classification using clustering-guided light gradient boosting machine

During tunnel boring machine(TBM)excavation,lithology identification is an important issue to understand tunnelling performance and avoid time-consuming excavation.However,site investigation generally lacks ground samples and the information is subjective,heterogeneous,and imbalanced due to mixed ground conditions.In this study,an unsupervised(K-means)and synthetic minority oversampling technique(SMOTE)-guided light-gradient boosting machine(LightGBM)classifier is proposed to identify the soft ground tunnel classification and determine the imbalanced issue of tunnelling data.During the tunnel excavation,an earth pressure balance(EPB)TBM recorded 18 different operational parameters along with the three main tunnel lithologies.The proposed model is applied using Python low-code PyCaret library.Next,four decision tree-based classifiers were obtained in a short time period with automatic hyperparameter tuning to determine the best model for clustering-guided SMOTE application.In addition,the Shapley additive explanation(SHAP)was implemented to avoid the model black box problem.The proposed model was evaluated using different metrics such as accuracy,F1 score,precision,recall,and receiver operating characteristics(ROC)curve to obtain a reasonable outcome for the minority class.It shows that the proposed model can provide significant tunnel lithology identification based on the operational parameters of EPB-TBM.The proposed method can be applied to heterogeneous tunnel formations with several TBM operational parameters to describe the tunnel lithologies for efficient tunnelling.

Experimental study on the membrane distillation of highly mineralized mine water

Membrane distillation(MD)is a promising membrane separation technique used to treat industrial wastewater.When coupled with cheap heat sources,MD has significant economic advantages.Therefore,MD can be combined with solar energy to realize the large-scale and low-cost treatment of highly mineralized mine water in the western coalproducing region of China.In this study,highly mineralized mine water from the Ningdong area of China was subjected to vacuum MD(VMD)using polyvinylidene fluoride hollow-fiber membranes.The optimal operation parameters of VMD were determined by response surface optimization.Subsequently,the feasibility of VMD for treating highly mineralized mine water was explored.The fouling behavior observed during VMD was further investigated by scanning electron microscopy with energy-dispersive X-ray spectroscopy(SEM-EDS).Under the optimal parameters(pressure=-0.08 MPa,temperature=70℃,and feed flow rate=1.5 L/min),the maximum membrane flux was 8.85 kg/(m^(2) h),and the desalination rate was 99.7%.Membrane fouling could be divided into three stages:membrane wetting,crystallization,and fouling layer formation.Physical cleaning restored the flux and salt rejection rate to 94%and 97%of the initial values,respectively;however,the cleaning interval and cleaning efficiency decreased as the VMD run time increased.SEM-EDS analysis revealed that the reduction in flux was caused by the precipitation of CaCO_(3).The findings also demonstrated that the membrane wetting could be attributed to the formation of NaCl on the cross section and outer surface of the membrane.Overall,the results confirm the feasibility of MD for treating mine water and provide meaningful guidance for the industrial application of MD.

Research on the Design of a De-oiling Compound Hydrocyclone and Its Separation Characteristics

A compound hydrocyclone is a new strategy for oil-water separation. It is based on the study of static and dynamic hydrocyclones. In this paper are introduced its geometric traits and separation mechanism. Experiments are carried out about the relationship between geometric parameters ＆ operating parameters and the separation efficiency of the compound hydrocyclone. Under experimental conditions, the appropriate structural parameters optimized are as follows： The rotating grid is of the straight board type, 3 straight vanes with a length of above 95 ram; the diameter of the overflow vent ranges 3-12 ram; the separation efficiency is better when the large conical angle of the static vortex body is about 20° and the small conical angle in the range of 1° -4° ： The separation effect is better under the following conditions： The rotary speed is 1,700-2,400 r/min; the disposal capacity is 5.5 m^3/h; the loss of working pressure is 0.05-0.25MPa; and the split ratio ranges 5%-15%. The experimental study provides a certain basis for the design andapplication of the compound hydrocyclone.

Machinability of Polymeric Composites and Future Reinforcements—A Review

This paper reviews the machinability and mechanical properties of natural fiber-reinforced composites. Coupling agents, operating parameters, as well as chemical treatment effects on natural fiber-reinforced composites’ machinability are also reviewed. Moreover, the impacts of fibers’ physical properties on the machinability of the composite are mentioned. Fiber volume fraction (Vf), fiber orientation as well as chemical treatment effects on mechanical properties are also defined. Conclusively, the effect of fibers’ physical properties as well as mechanical properties is described. It was discovered that chemical treatment of natural fibers improved their compatibility with the matrix by removing their surface tissues, increasing the roughness average (Ra), and reducing moisture absorption. Also, the Orientation of the fiber plays an important role in controlling the mechanical properties of the composite. Moreover, some physical properties of the fibers, including quality of fiber distributed in the matrix;fiber size, length, and diameter;moisture absorption;porosity and the way fibers break during compounding with the matrix, were found to affect the mechanical properties of the composites formed.

Prediction of Disc Cutter Life During Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network

Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision.This study proposes a new model to estimate the disc cutter life(Hf)by integrating a group method of data handling(GMDH)-type neural network(NN)with a genetic algorithm(GA).The efficiency and effectiveness of the GMDH network structure are optimized by the GA,which enables each neuron to search for its optimum connections set from the previous layer.With the proposed model,monitoring data including the shield performance database,disc cutter consumption,geological conditions,and operational parameters can be analyzed.To verify the performance of the proposed model,a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model.The results indicate that the hybrid model predicts disc cutter life with high accuracy.The sensitivity analysis reveals that the penetration rate(PR)has a significant influence on disc cutter life.The results of this study can be beneficial in both the planning and construction stages of shield tunneling.

Investigation of feature contribution to shield tunneling-induced settlement using Shapley additive explanations method

Accurate prediction of shield tunneling-induced settlement is a complex problem that requires consideration of many influential parameters.Recent studies reveal that machine learning(ML)algorithms can predict the settlement caused by tunneling.However,well-performing ML models are usually less interpretable.Irrelevant input features decrease the performance and interpretability of an ML model.Nonetheless,feature selection,a critical step in the ML pipeline,is usually ignored in most studies that focused on predicting tunneling-induced settlement.This study applies four techniques,i.e.Pearson correlation method,sequential forward selection(SFS),sequential backward selection(SBS)and Boruta algorithm,to investigate the effect of feature selection on the model’s performance when predicting the tunneling-induced maximum surface settlement(S_(max)).The data set used in this study was compiled from two metro tunnel projects excavated in Hangzhou,China using earth pressure balance(EPB)shields and consists of 14 input features and a single output(i.e.S_(max)).The ML model that is trained on features selected from the Boruta algorithm demonstrates the best performance in both the training and testing phases.The relevant features chosen from the Boruta algorithm further indicate that tunneling-induced settlement is affected by parameters related to tunnel geometry,geological conditions and shield operation.The recently proposed Shapley additive explanations(SHAP)method explores how the input features contribute to the output of a complex ML model.It is observed that the larger settlements are induced during shield tunneling in silty clay.Moreover,the SHAP analysis reveals that the low magnitudes of face pressure at the top of the shield increase the model’s output。

VOLUMETRIC MASS TRANSFER COEFFICIENT BETWEEN SLAG AND METAL IN COMBINED BLOWING CONVERTER

The effects of operation parameters of combined blowing converter on the volumetric mass transfer coefficient between slag and steel are studied with a cold model with water simulating steel, oil simulating slag and benzoic acid as the transferred substance between water and oil. The results show that, with lance level of 2.1m and the top blowing rate of 25000Nm3/h, the volumetric mass transfer coefficient changes most significantly when the bottom blowing rate ranges from 384 to 540Nm3/h. The volumetric mass transfer coefficient reaches its maximum when the lance level is 2.1m, the top blowing rates is 30000Nm3/h, and the bottom blowing rate is 384Nm3/h with tuyeres located symmetrically at 0.66D of the converter bottom.