Extraction Unit Research Articles

Thermo-Economic Assessment of Photovoltaic/Thermal Pan-Els-Powered Reverse Osmosis Desalination Unit Combined with Preheating Using Geothermal Energy

Recently, the reverse osmosis (RO) process is widely used in the field of desalinating brackish water and seawater to produce freshwater, but the disadvantage of using this technology is the increase in the rates of electrical energy consumption necessary to manage these units. To reduce the rates of electrical energy consumption in RO desalination plants, geothermal energy and photovoltaic/thermal panels were used as preheating units to heat the feed water before entering RO desalination plants. The proposed system in this study consists of an RO desalination plant with an energy recovery device, photovoltaic/thermal panels, and a geothermal energy extraction unit. To evaluate the system performance, three incorporated models were studied and validated by previous experimental data. The results indicated that incorporating the geothermal energy and photovoltaic/thermal panels with the RO desalination plants has positive effects in terms of increasing productivity and reducing the rates of specific power consumption in RO desalination plants. The average saving in the specific power consumption for utilizing the thermal recovery system of PV panels and geothermal energy as preheating units reached 29.1% and 40.75% for the treatment of seawater and brackish water, respectively. Additionally, the economic feasibility showed the saving in the cost of freshwater produced from the RO desalination plants for incorporating both geothermal energy and photovoltaic panels with a thermal recovery system with reverse osmosis desalination plants of up to 39.6%.

Development of Locally Fabricated Honey Extracting Machine

An indigenous honey extracting machine was designed, fabricated and evaluated at the workshop of the department of Agricultural Technology, Federal Polytechnic, Ile-Oluji, Nigeria using locally available materials. It has the extraction unit, the presser, the honey collector and the frame. The harvested honeycomb is placed inside the cylindrical extraction unit with perforated holes. The press plate is then positioned on the honeycomb while the hydraulic press of 3 ton was placed on it and supported by the frame. The hydraulic press which is manually operated forces the oil out of the extraction chamber through the perforated holes into the honey collector positioned at the lowest part of the extraction chamber. The honey extracting machine has an average throughput of 2.59 kg/min and efficiency of about 91%. The cost of producing the honey extracting machine is NGN 31, 700: 00, thirty-one thousand, and seven hundred naira only or $70 at NGN 452.8 to a dollar. This cost is affordable to beekeepers and would-be honey entrepreneurs. The honey extracting machine is easy to operate and maintained without any complex technical know-how. This simple technology solves the challenge of honey extraction among our peasant farmers in Nigeria.

Validation of a Novel Supercritical Fluid Extractor/Dryer Combo Instrument.

The current pharmaceutical manufacturing scenario involves different techniques for the processing of materials. For example, the extraction unit is one of the essential aspects of plant-based pharmaceuticals. Recently, various kinds of extraction techniques have been used for analytical and preparative scales; among them, a supercritical fluid extractor (SCFE) is the most widely used technique for extraction. It is used for an extensive range of crude drugs and can be possible with the help of SCFE by varying temperature/pressure. Notably, it uses carbon dioxide (CO2) for extraction instead of other solvents. Simultaneously, lyophilization is an important technique used at different processing steps along with other methods. In lyophilization, CO2 is used as a cooling agent in the shelves of lyophilized equipment. It behaves as a supercritical fluid at critical pressure (Pc) of 72.7 atm and critical temperature (Tc) of 31°C. Considering the criteria mentioned earlier, there is a possibility that liquid CO2 or supercritical carbon dioxide (SC-CO2) can be used as a cooling agent in a lyophilizer and extraction solvent in SCFE. This review presents a brief outline for the possible validation parameters of the proposed novel processor; that is, SCFE/Dryer combo instrument containing Design Qualification, Installation Qualification, Operational Qualification, and Performance Qualification.

Optimization of supercritical fluid CO2 extraction of Opoponax essential oil (C. erythraea)

Essential oil of Opoponax (Commiphora erythraea) was extracted by means of steam distillation and supercritical CO2 (SFE-CO2) extraction. Experiments were performed using a Clevenger-type apparatus. On the other hand, SFE-CO2 runs were carried out using OCOLABS extraction unit at the operating conditions of 150, 200, and 250 bars and within temperature range from 50 to 65 °C. Other extraction parameters such as particle size and extraction period were also investigated in order to validate their effect on the processes and the oil yield. The extraction yields were almost similar (2.46%) for the optimized SFE-CO2 at 250 bar, 65 oC, and 2 hr, and 2.45% for the steam distillation. Our findings confirmed that the grinding of the material (particle size), pressure, and temperature had an important effect on the SFE-CO2 extraction process. GC/MS analyses revealed that major compounds, namely α-santalene, α and β-bisabolone, and trans-alpha-bergamotene were of similar quantities except for ocimene. α-Santalene concentration was 7.15% for steam distillation and 15.32% for SFE-CO2 at the abovementioned conditions.

Lab in a bottle, open-source technologies for the design of affordable environmental samplers integrating on-site extraction

Access to clean water, a Sustainable Development Goal, can only be accomplished worldwide using affordable technologies. In the analytical chemistry realm, developing cheap devices capable of on-site extraction results in a helpful tool. This article presents a new device based on open-source technologies for the on-site extraction of isoflavones from environmental waters as a proof of concept. The device is wholly operated by an Arduino microcontroller that also integrates sensors (temperature and conductivity) to provide on-site information. The extraction unit stirs into the sample, using a small motor, a planar sorptive phase consisting of Mixed-mode Anionic Exchange particles (MAX). After the on-site extraction, the sorptive phase is transported to the lab for its final analysis by liquid chromatography coupled with mass spectrometry. The analytical method provided detection limits of 1.5 ng L−1 (biochanin A and formononetin) and 15 ng L−1 (daidzein and genistein). The precision, expressed as relative standard deviation, was evaluated at three concentration levels providing results better than 9.8 %. The accuracy, with relative recoveries in the 85–119 % ranges, supports the application of the method. The relevance of isotopically labeled compounds to normalize the effect of some variables on the extraction of the analytes is also discussed in detail.

Design consideration and optimization of process parameters in fiber extraction unit via modelling studies

AbstractDemand for natural fibers as an alternative to synthetic fibers is on the rise. Constraints of current fiber extraction units are limited to single raw material and has low energy efficiency. Hence, this study was taken up for the design and development of a multiple fiber extractor, modelling and optimization of the machine operating parameters, and economic analysis of the developed prototype. The designed machine operates based on the raspador principle. Box Behnken design (BBD) and artificial neural network (ANN) were used to evaluate the performance and optimize the variables of the designed multiple fiber extractor. The input variables included roller speed (550, 735, and 920 rpm), gap/clearance of the roller (2, 3, and 4 mm) and the pitch/distance between the spline slots (0.5, 1.5, and 2.5 mm), while the output variables were yield (kg), capacity (kg/h), energy consumption (kJ), and efficiency (%).The prediction accuracy of quadratic models (R2 = 0.962–0.985) developed in BBD were better than the ANN models (R2 = 0.831–0.946). The optimized combination was found to be 714.23 rpm roller speed, 3.12 mm gap, and 1.49 mm pitch of the blade, which yielded (1.48 kg) a desirability value of 0.95. The XRD results indicated the highest crystallinity index was recorded for Pineapple leaf fiber compared to banana fiber. SEM results revealed that extracted BF having typical network structure (cellulose, hemicellulose, lignin, and waxes) compared to smooth PLF (lignin and hemicellulose). The fibers extracted using the developed machine was utilized for making biodegradable cutlery.Practical ApplicationsThis study was taken up to design a multifiber extractor, to extract fibers from agricultural residues after their useful parts are taken for consumption or processing. Thus, a single machine capable of handling multiple byproducts can utilize those residues, which were previously discarded as waste. Moreover, the extracted fibers are used for the manufacture of biodegradable cutlery. With a small investment, this machine can provide an additional income to the farmers, along with taking away the concern regarding waste disposal.

Indoor Geomagnetic Positioning Using Direction-Aware Multiscale Recurrent Neural Networks

Accurate indoor positioning has become an indispensable technology for location-based service in indoor environments. Geomagnetic field fingerprinting for indoor positioning is an attractive alternative to Wi-Fi and Bluetooth because of the omnipresent signals and the infrastructure-free mode. However, heterogeneous devices and users with fluctuant geomagnetism and massive crowdsourced data covering large-scale indoor scenes may result in a low degree of discernibility and degrade the rerecognizable performance for pedestrian positioning. To overcome the problem, a deep-learning-based indoor geomagnetic positioning method with direction-aware multiscale recurrent neural networks (DM-RNNs) is presented. First, the direction information is taken into the fingerprint construction and localization process to increase spatial identification, which is necessary for indoor pedestrian navigation. Second, instead of using a single holistic feature from sequence fingerprints directly, we employ the different scale-based feature extraction units for variational anomalies of the signal by using multiscale RNNs, increasing the model adaptability and generality for random pedestrian motion and device heterogeneity. Third, the ensemble learning mechanism is adopted in the model to obtain robust localization results. Furthermore, we employ the convenient visual-inertial odometry (VIO) to collect the geomagnetic signal dataset and utilize the sequence augmentation approach to generate synthesized trajectories from multiple single-point magnetic values for training and testing the model. Extensive experiments in three different indoor scenes demonstrate that the proposed approach outperforms state-of-the-art competing schemes by a wide margin, reducing mean localization error by more than 30% and obtaining over 79% direction estimation precision in different indoor scenarios.

Radar Signal Recognition Based on Multilayer Perceptron Neural Network

Low Probability of Intercept (LPI) radars are developed on an advanced architecture by making use of coded waveforms. Detection and classification of radar waveforms are important in many critical applications like electronic warfare, threat to radar and surveillance. Precise estimation of parameter and classification of the type of waveform will provide information about the threat to the radar and also helps to develop sophisticated intercept receiver. The present work is on classification of modulation waveforms of LPI radar using multilayer perceptron neural (MLPN) network. The classification approach is based on the following two steps. In the first step, the waveforms are analysed using cyclstationary technique which models the signal in bi-frequency (BF) plane. Using this algorithm, the BF images of the signals are obtained. In the second step, the BF images are fed to a feature extraction unit to get the salient features of the waveform and then to the multilayer perceptron neural (MLPN) network for classification. Nine types of noise free modulation waveforms (Frank, four polyphase codes and four poly time codes) are classified using the images obtained in the first step. The success rate achieved is 100 % for noise free signals. The experiment is repeated for various noise levels up to -12dB SNR. The noisy signals, before feeding to the MLPN network, are denoised using two types of denoising filters connected in cascade and the classification success rate achieved is 93.3% for signals up to -12dB SNR.

Efficient conversion of glucose to 5-hydroxymethylfurfural by synergetic catalysis of enzymes and chemical catalysts towards potential large-scale continuous operation

A continuous process to efficiently produce 5-hydroxymethylfurfural (HMF) from glucose by synergetic catalysis of enzymes and chemical catalysts was developed in dimethyl sulfoxide (DMSO)/water system under mild conditions. Sweetzyme IT Extra and Amberlyst 36 were used as heterogeneous catalysts for glucose isomerization and fructose dehydration, respectively, in packed bed reactors. Fructose yield of 50.9 % was obtained during glucose isomerization in 1:1 VDMSO/VWater medium at 60 °C and residence time of 30 min. Selective dehydration of fructose in glucose-fructose mixture was achieved in DMSO medium with fructose conversion of higher than 97 % and HMF yield of 87.2 % at 85 °C and a residence time of 40 min, where 93 % glucose unconverted. By adjusting the water content in the reaction system and recycling of water from isomerization and extraction units, the catalysts could keep good stability for 150 batches reuse without loss of activity. Based on the converted glucose, glucose conversion, HMF yield and selectivity reached 100 %, 81.5 %, and 81.5 %, respectively. This process showed great promise for production of HMF from glucose in continuous operation at large scale.

Denoising Method for Seismic Co-Band Noise Based on a U-Net Network Combined with a Residual Dense Block

To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic co-band denoising model Atrous Residual Dense Block U-Net (ARDU), which uses a U-shaped convolutional neural network (U-Net) as a basic framework and combines atrous convolution and the residual dense block (RDB). In the ARDU model, atrous convolution is connected with residual dense blocks to form the feature extraction unit of the model encoder. Among them, the residual dense blocks can deepen the network’s depth and enhance the feature extraction ability of the network on the premise of mitigating the gradient-vanishing and gradient-exploding problem. Atrous convolution can enlarge receptive fields, reduce waveform distortion, and protect effective signals without increasing network parameters. To test the denoising performance of the ARDU model, the Stanford Global Seismic dataset was used to construct a training set and a test set and the model was trained and tested on it. The experimental results of the ARDU model for different types of seismic co-band noise showed that this model can effectively remove seismic co-band noise, protect effective signals, improve the SNR of seismic signals, and enhance the quality of seismic signals. To further verify the denoising effect of the model, this model was compared with the wavelet threshold denoising U-Net model and the denoising residual dense block (DnRDB) model, and the results showed that the ARDU model has the best SNR, r (correlation coefficient), and root-mean-square error (RMSE) and the least distortion of the seismic signal waveform.

Middle Eastern projects move forward

A pair of big Middle Eastern petrochemical projects have been given the go-ahead. Saudi Aramco and TotalEnergies have made the final decision to invest in a new cracker complex in Jubail, Saudi Arabia. The $11 billion complex will be integrated with the firms’ existing refining joint venture there. The cracker will have capacity to make 1.7 million metric tons (t) of ethylene per year. The project will also include two downstream polyethylene plants and a butadiene extraction unit. It will be majority owned by Aramco and go on line in 2027. Separately, Chevron Phillips Chemical and QatarEnergy have green-lighted a $6 billion project for Ras Laffan, Qatar. It will have a 2.1 million t per year ethylene cracker, the largest in the region, as well as downstream polyethylene plants.

Simultaneous quantification of Zr, Mo, U, Np and Pu in technological solutions of spent nuclear fuel reprocessing with a potentiometric multisensor system

The chemical analysis of spent nuclear fuel (SNF) reprocessing is a very challenging research field due to the high radioactivity of process streams and their complex composition. The process managing requires thorough chemical analysis to ensure safe and efficient performance. Traditional ways of chemical control of SNF reprocessing are based on sampling methods and involve long and tedious procedures, failing to provide immediate information on the process status. Recent literature suggests that potentiometric multisensor systems can be employed for on-line control. However, up to now these systems were only studied in very simple model media. This work reports on the potentiometric multisensor array applied for the first time for the simultaneous quantification of the content of actinides (uranium, plutonium, neptunium) and several other analytes (zirconium, molybdenum and nitric acid) in the complex samples obtained from the pilot extraction unit for SNF reprocessing. The same samples were also analyzed by UV-Vis spectrometry (another prospective method for on-line control) and the analytical performance of these two methods was compared using established analytical figures of merit (sensitivity and analytical sensitivity metrics) for multivariate calibration. In spite of the extremely challenging analytical task, both simple methods provide the characteristics suitable for real industrial applications.

An Effective Self-Attention-Based Hybrid Model for Short-Term Traffic Flow Prediction

Vehicle exhaust is one of the main sources of carbon emissions. The short-term traffic flow prediction plays an important role in alleviating traffic congestion, optimizing the travel structure, and reducing traffic carbon emissions. The current advanced models of short-term traffic flow prediction are evaluated in this work, especially their inadequacies. To improve the prediction accuracy and ensure fine traffic management, an effective self-attention-based hybrid model is proposed to predict the short-term traffic flow. The proposed model includes an encoder-decoder neural network module and a self-attention mechanism module. The self-attention mechanism module is applied as a feature extraction unit in this hybrid model to enhance the ability of key information capture and to settle the problem on key information disappearing due to the increasing sequence length in traditional models. The dataset of the Guangdong freeway toll station is used for the experimental testing. Compared with several baseline models, the proposed model is more suitable for real-time prediction and can provide highly accurate results. Also, a better interpretability is presented in this proposed model. The experimental results showed that MAE, RMSE, and MAPE of the proposed model are 3.01, 4.38, and 12.99%, respectively. Our new hybrid model gives a higher accuracy than the support vector regression (SVR) model, LSTM neural network-attention (LSTM-attention) model, and temporal convolutional network (TCN) model. It shows that the proposed model in this work is favorable to the short-term traffic flow prediction.

A Topic-Specific Web Crawler using Deep Convolutional Networks

This paper presented a new focused crawler that efficiently supports the Turkish language. The developed architecture was divided into multiple units: a control unit, crawler unit, link extractor unit, link sorter unit, and natural language processing unit. The crawler's units can work in parallel to process the massive amount of published websites. Also, the proposed Convolutional Neural Network (CNN) based natural language processing unit can professionally classifying Turkish text and web pages. Extensive experiments using three datasets have been performed to illustrate the performance of the developed approach. The first dataset contains 50,000 Turkish web pages downloaded by the developed crawler, while the other two are publicly available and consist of “28,567” and “22,431” Turkish web pages, respectively. In addition, the Vector Space Model (VSM) in general and word embedding state-of-the-art techniques, in particular, were investigated to find the most suitable one for the Turkish language. Overall, results indicated that the developed approach had achieved good performance, robustness, and stability when processing the Turkish language. Also, Bidirectional Encoder Representations from Transformer (BERT) was found to be the most appropriate embedding for building an efficient Turkish language classification system. Finally, our experiments showed superior performance of the developed natural language processing unit against seven state-of-the-art CNN classification systems. Where accuracy improvement compared to the second-best is 10% and 47% compared to the lowest performance.

TA-LSTM: A Time and Attribute Aware LSTM for Deep Flight Track Clustering

The rapid development of civil aviation imposes an urgent need for efficient air traffic management. Flight track clustering is the premise and foundation of air traffic control. Flight track points compose time-series data with non-identical point- to-point intervals, and the effective latent representation of track data is key to the flight trajectory clustering. Long Short-Term Memory (LSTM) is an important and popular method to extract the features of trajectory time-series data. However, LSTM ignores the irregular time intervals between track points and fails to effectively distinguish the importance of different attributes at each track point during feature extraction. In addition, the existing methods based on LSTM either ignore the distinct impacts of different attributes at each track point or simply assume that the influences of the previous track points on the subsequent ones decrease with time. In this paper, we propose a flight trajectory feature extraction unit called TA-LSTM (Time and Attribute Aware Long Short- Term Memory) and a flight track clustering model based on TA- LSTM. The flight track clustering model consists of a feature extraction layer and a clustering layer. The feature extraction layer adopts TA-LSTM to obtain the latent representation of flight tracks, which adds a time control gate and an attribute control gate to the standard LSTM unit. The time control gate manipulates the action of each track point on subsequent points to obtain the characteristics of irregular time intervals. The attribute control gat enables LSTM to capture the impact of different attributes on the flight track features at various points. The clustering layer takes the latent representation of flight tracks as the input to obtain the clustering results. The clustering model updates the parameters of TA-LSTM and the cluster centers by minimizing the Kullback-Leibler (K-L) divergence. We conduct experiments using Automatic Dependent Surveillance-Broadcast track data provided by VariFlight. In comparison with popular and state-of-the-art methods, the proposed method obtains superior pe formance in three widely used evaluation metrics, i.e., Silhouette Coefficient, Calinski-Harabaz index and Davies-Bouldin index.

Factors Related to Suicidal Ideation by Gender and Age Group in Korean Adults.

According to the results of the age-standardized suicide rate (suicide deaths per 100,000 population) published by Organization for Economic Cooperation and Development countries from 2003 to 2019, the suicide rate in Korea is ranked first almost every year, so it should be managed at the national level. To reduce the suicide rate, many scholars are studying suicide. The suicidal process begins with suicidal ideation, progresses through suicide attempts, and ends with suicide. Among them, the frequency of suicidal thoughts was found to be highly correlated with suicide plans and attempts, and it is said that 60% of those who change from suicidal ideation to attempts appear within one year. Therefore, research related to suicidal ideation to lower the suicide rate will contribute to preventing suicide at an early stage. This study used data from the Korea National Health and Nutrition Examination Survey (KNHANES). Among them, data from 2013, 2015, 2017, and 2019 surveyed adults were compared for suicidal ideation among 18,339 adults. Considering the characteristics of the KNHANES data, complex sample analysis was performed considering the primary extraction unit (region), stratification variables, and weights. The Rao-Scott χ² test was stratified by age group to confirm the distribution of suicidal ideation according to general characteristics. Univariate and multiple logistic regression analyses were performed to understand the effect on suicidal ideation. Among all subjects, the rate of suicidal ideation was 4.75%, and among the age groups, the rate of suicidal thoughts was the highest among those over 80 years old (10.39%). Compared to those who were 50s, those in their 30s had the lowest suicidal ideation (odds ratio [OR], 0.52; 95% confidence interval [CI], 0.39-0.70), and those in their 80s had the highest rate of suicidal ideation (OR, 1.97; 95% CI, 1.38-2.82). Except for those in their 20s, the rate of suicidal ideation tends to increase with age. Overall, the lower 25% of the Euro Quality of Life-5 Dimensions (EQ-5D) index, depression lasting more than two weeks, and subjects with poor subjective health or high stress were more likely to have suicidal ideation. In particular, EQ-5D Index (OR, 5.86; 95% CI, 3.73-9.20), depressive symptoms (OR, 19.65; 95% CI, 9.94-38.83) in 20s, and stress in 80s (OR, 10.87; 95% CI, 5.63-20.96) was the highest, and those had the good subjective health perception in 30s (OR, 0.10; 95% CI, 0.05-0.20) was lowest. Participants in their 30s, 40s, and 60s who were divorced or widowed and those in their 50s and 60s who had never married or lived together were more likely to consider suicide ideation. Except for those in their 20s and 80s, the rate of suicidal ideation tends to increase with lower household income levels. In the results of this study, it can be seen that a combination of factors such as a person's personality traits, stress or coping ability to various events, social support, and mental or physical limitations influence suicidal behavior. Suicidal ideation is not simply affected by a single factor but may be influenced by multiple factors.

МЕТОДИЧЕСКИЙ ПОДХОД К ОБОСНОВАНИЮ ТЕХНОЛОГИЙ СНИЖЕНИЯ УЩЕРБА ОТ ПЕРЕИЗМЕЛЬЧЕНИЯ РУДЫ ПРИ ПОДЗЕМНОЙ РАЗРАБОТКЕ

In underground mining, in most cases, there is a decrease in the completeness of extraction of subsoil reserves and the efficiency of geotechnology associated with the regrinding of ore in the process of mining. Consequently, the development of a methodology for substantiating measures to reduce damage from ore regrinding, which allows taking into account the diversity and degree of influence of acting factors at the design stage of a mining system, is an urgent scientific and technical task. Based on the use of a systematic approach and the modular principle of designing mining systems, it is proposed to separate such measures into a single geotechnological module within the subsystem of a stope excavation. The expediency of such a separating is substantiated, since the measures meet the criterion of independence, and their presence or absence significantly affects the efficiency and safety of the mining system. To improve the objectivity of the substantiation of technologies at the stage of designing a mining system, a methodological approach is proposed, which consists in taking into account the category of lost ore fines and the state of its presence in the mining area, which are formed during the extraction of the main reserves of an extraction unit of mining technical conditions and the procedure for implementing geotechnology of reduce damage from ore regrinding relative to stoping cycle.

Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is an important but challenging topic in the field of remote sensing and earth observation. By coupling the advantages of convolutional neural network (CNN) and Transformer model, the CNN–Transformer hybrid model can extract local and global features simultaneously and has achieved outstanding performance in HSI classification. However, most of the existing CNN–Transformer hybrid models use artificially specified hybrid strategies, which have poor generalization ability and are difficult to meet the requirements of recognizing fine-grained objects in HSI of complex scenes. To overcome this problem, we proposed a convolution–Transformer adaptive fusion network (CTAFNet) for pixel-wise HSI classification. A local–global fusion feature extraction unit, called the convolution–Transformer adaptive fusion kernel, was designed and integrated into the CTAFNet. The kernel captures the local high-frequency features using a convolution module and extracts the global and sequential low-frequency information using a Transformer module. We developed an adaptive feature fusion strategy to fuse the local high-frequency and global low-frequency features to obtain a robust and discriminative representation of the HSI data. An encoder–decoder structure was adopted in the CTAFNet to improve the flow of fused local–global information between different stages, thus ensuring the generalization ability of the model. Experimental results conducted on three large-scale and challenging HSI datasets demonstrate that the proposed network is superior to nine state-of-the-art approaches. We highlighted the effectiveness of adaptive CNN–Transformer hybrid strategy in HSI classification.

A Data-Driven Fine-Management and Control Method of Gas-Extraction Boreholes

In order to improve the efficiency of gas extraction in coal mines, a data-driven fine-management and control method for gas extraction is proposed. Firstly, the accurate prediction of coal seam thickness and gas content was used to evaluate the gas reserves. Based on the time relationship between mining activities and gas extraction, the calculation model of borehole distance in different extraction units is established, and the differential borehole design is realized. Then, a drilling video-surveillance system and drilling trajectory measurement device are used to control the drilling process and the construction effect. Finally, the model of extraction data-correction and the identification of failed boreholes is established, then the failed boreholes are repaired. The technology method provided in the paper has realized the fine control of gas-extraction borehole design, construction, measurement, and repair, and formed a more scientific gas-extraction borehole control technology system, which provides new thought for efficient gas extraction.

Upcycling wastewater nitrate into ammonia fertilizer via concurrent electrocatalysis and membrane extraction

Electrocatalytic upcycling of nitrate (NO3−) in wastewater into the valuable ammonium-based fertilizer has been considered as a green and appealing alternative to biological nitrogen removal or the industrial ammonia (NH3) production processes. This work investigated an innovative and energy-efficient electrolysis flow-through cell consisting of a porous activated iron nickel (FeNi) alloy cathode and a hollow polypropylene fiber membrane extraction unit to realize the electrochemical NO3− reduction and simultaneous upcycling to ammonium sulphate salts ((NH4)2SO4). Cathodic and anodic electrochemical half-reactions were coupled to enable NO3− reduction to ammonia and in-situ acid/base productions to promote the membrane stripping of NH3. Our results show that after electrolysis operation for 14 h for a synthetic wastewater containing 150 mM NO3− under a cathodic current density of 30 mA·cm−2, 99 % of NO3− removal efficiency, 98 % of ammonia selectivity, 93 % of Faradic efficiency and 97 % of total ammonia nitrogen (TAN) recovery were achieved respectively. A NH3 recovery flux and a specific energy consumption reached 2050 g-(NH4)2SO4·m−2·d−1 and 11 kWh·kg−1-(NH4)2SO4, respectively, which outcompetes many reported processes. Direct electron transfer was the main mechanisms of electrochemical NO3− reduction to ammonia. Interfacial reaction thermodynamics and kinetics analysis of key intermediates (e.g., *NO3, *NO2, and *NO) shows that the NiFe2O4 (3 1 1)-Ni site on the thermally activated FeNi alloy surface exhibited higher reactivity and specificity toward electrochemical NO3− reduction to ammonia over nitrogen (N2) or hydrogen (H2) generation. Ultimately, this study aims to promote sustainable nitrogen nutrient recovery and ammonia fertilizer synthesis from wastewater treatment.