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Improving the quality and efficiency of industrial land is an important starting point for changing the development mode, optimizing the economic structure, transforming the growth momentum and improving administrative efficiency in China. On the basis of reviewing the evolution of the policy of upgrading and increasing efficiency of industrial land, combined with the requirements of high-quality development, this paper analyzes the core concept of upgrading and increasing efficiency of industrial land, summarizes the practice at home and abroad and refines the model, analyzes the problems faced by upgrading and increasing efficiency of industrial land at present and puts forward relevant suggestions. It is found that the improvement of industrial land quality and efficiency involves core concepts such as land quality improvement, land supply mode optimization, policy innovation and implementation, technological innovation and application, environmental protection and sustainable development. At present, various modes and ways have been formed around the core concepts in the relevant practices of industrial land quality improvement and efficiency at home and abroad. At present, industrial land upgrading and efficiency improvement are facing many problems, such as low land use intensity, single supply mode, inadequate supervision after supply, imperfect exit and revitalization mechanism, and insufficient policy and system guarantee. In the future, we need to comprehensively improve the implementation path from planning guidance, land supply, industrial transformation, exit and reuse, supervision and law enforcement, and system guarantee. [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Steam generator tube rupture (SGTR) accident is one of the accident scenarios that must be considered during the design and safety analysis process of lead-cooled fast reactors (LFR). Focused on this topic, numerous experiments and theoretical research have been conducted in recent decades, which could be generally categorized into two aspects: on the one hand, separation effect experiments and theoretical analysis focusing on the specific phenomena of lead-water interaction process; on the other hand, engineering test facilities and numerical simulations focusing on the overall process of LFR SGTR accident, mainly concentrated on the evolution of macro parameters such as system pressure and lead pool temperature during SGTR accident. In this paper, firstly, the basic mathematical and physical models of ACENA code was introduced; then the code was validated against the LBE-N2 two-phase flow experiment HESTIA-2, the KYILN-Ⅱ-S LBE-H2O interaction experiment and the analytical solution of the point-kinetic neutronic equations; after that the Eulerian multi-phase analysis code ACENA was applied to simulate the postulated unprotected SGTR process of European pool-type lead-cooled fast reactor ALFRED. Attention was paid to the simulated migration process of steam bubbles in the lead pool as well as the evolution of safety parameters such as maximum cladding temperature, fuel centerline temperature, relative nuclear power and reactor vessel pressure. Furthermore, the sensitivity of ruptured tube quantities, lead coolant circulation paths and neutron dynamics parameters on calculation results were analyzed. This paper’s study demonstrates that the Ishii-Chawla-Suzuki interfacial drag coefficient model could accurately predict the diffusion and migration characteristics of rising bubbles in circular/annular LBE flow channels when combined with the interfacial area concentration transport model proposed by Ishii et al; The simulation of KYLIN-Ⅱ-S experiment shows that ACENA code could reasonably reproduce phenomena such as system pressure fluctuations and temperature transients of liquid lead during the interaction process between LBE and water; the calculated results of ALFRED reactor’s key thermal-hydraulic parameters under hot full power condition by ACENA code are generally consistent with results obtained by internationally recognized one-dimensional system code TRACE/FRED, proving the reliability of ACENA code when applied to full reactor system level; The calculation of the hypothetical SGTR accident of the ALFRED reactor verifies the ACENA code’s capability of simulating the complex multi-component multi-phase flow, and the calculation results reveal that designing the primary coolant flow path rationally and reducing the number of ruptured pipes are of great significance to mitigating the potential consequences of LFR SGTR accidents. This work can provide reference for the safety analysis work of China’s pool-type lead-cooled fast reactors.

The full digital simulation plays a crucial role in the design, testing evaluation, even combat training of electro-optical imaging-guided weapons. In order to solve the problem of lacking mathematical models for the seeker in the full digital simulation test of electro-optical imaging-guided weapons, this paper proposes a modeling approach for the seeker based on the concept of digital twins, which integrates mechanism and data. Taking typical infrared imaging seeker as an example, this paper constructs the mechanism model of seeker detection and identification process in basis of the principle of infrared detection and identification, then builds the data model of seeker guiding command based on the actual measurement datasets of the infrared imaging seeker under different working scenarios. Finally, based on this "mechanism+data" fusion model, the full digital simulation tests are conducted under different interference conditions for two typical combat scenarios of fixed target and moving target. The test results show that the mechanism and data fusion model can more realistically reflect the performance characteristics of infrared imaging seeker under complex confrontation scenarios.

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation. [ABSTRACT FROM AUTHOR]

Copyright of Aero Weaponry is the property of Aero Weaponry Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a Whale Optimization Algorithm (WOA) based on the Lévy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters; this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot. [ABSTRACT FROM AUTHOR]

The digital twin (DT) technology is currently considered a key technology for the digital representation of real-world systems. The application of DT technology in smart manufacturing can provide accurate model support for the analysis of mechatronic equipment applications based on model simulations. However, for such a mechanics-electric-hydraulic-control coupled complex system of mechatronics equipment, how to quickly and effectively construct its consistent multi-domain DT mechanism model has become the biggest obstacle to the wide application of DT technology in this field. Therefore, based on the synthesis of existing model construction methods for mechatronics equipment, this paper proposes multi-domain, multi-level, parametric, and consistent mechatronics equipment DT mechanism model construction guidelines. Based on the proposed model construction guidelines, a DT mechanism model construction process for mechatronics equipment is given. Finally, a consistent multi-domain DT mechanism model of computer numerical control machine tools (CNCMT) is constructed by Simscape, to verify the feasibility of the proposed method. The solutions in this paper provide a reliable, rapid, and consistent mechanism model along with construction guidelines and theoretical systems for engineers or researchers that use DT technology to solve specific application problems. Also, the implementation of the virtual commissioning application case in this paper provides application guidance for service analysis based on the DT model. [ABSTRACT FROM AUTHOR]

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Petroleum Refinery Engineering is the property of Petroleum Refinery Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications. [ABSTRACT FROM AUTHOR]

Drilling-fluid loss has always been one of the challenging issues in the field of drilling engineering. This article addresses the limitations of a single fluid-loss pressure mechanism model and the challenges in predicting positive drilling-fluid-loss pressure. By categorizing fluid losses of various types encountered during drilling, different geological formations associated with distinct mechanisms are considered. The actual drilling-fluid density in the wellbore at the time of fluid-loss occurrence is taken as a reference value for calculating the positive drilling-fluid-loss pressure of the already drilled well. Building upon this foundation, a combined model utilizing the Sparrow Search Algorithm (SSA) and Long Short-Term Memory (LSTM) neural network is constructed. This model effectively explores the intricate nonlinear relationship between well logging, logging engineering data, and fluid-loss pressure. By utilizing both data from the already drilled wells and upper formation data from ongoing drilling, precise prediction of positive drilling formation fluid-loss pressure can be achieved. Case studies demonstrate that the approach established in this paper, incorporating upper formation data, reduces the average absolute percentage error of fluid-loss pressure prediction to 2.4% and decreases the root mean square error to 0.0405. Through the synergy of mechanistic models and data-driven techniques, not only has the accuracy of predicting positive drilling formation fluid-loss pressure has been enhanced, but also valuable insights have been provided for preventing and mitigating fluid losses during drilling operations. [ABSTRACT FROM AUTHOR]

The operation of coal-fired power plants generates a large amount of wastewater. With the issuance of increasingly strict drainage standards, the cost of wastewater treatment is increasing, and the need to reduce the cost of wastewater treatment is becoming increasingly urgent. Thus, based on the principles of reverse osmosis (RO) and mechanical vapor recompression (MVR) in wastewater treatment, the operational optimization of an RO-MVR joint system was studied in this work with the consideration of reducing the operating costs of wastewater treatment under given operational conditions. Firstly, based on the basic principles of RO and MVR, corresponding mechanism models were established and their accuracy was verified. Then, an economic model of the RO-MVR joint system was established, with the goal of minimizing the water production unit price and daily operating costs of the joint system for optimization analysis. Finally, we analyzed the cost and water production performance of the RO-MVR joint system before and after optimization under different operating conditions. The results show that this optimization based on the RO-MVR joint system will reduce the unit price of water production to 3.16 CNY/m3, with the daily operating costs being decreased by 22% compared to before optimization. This result helps to reduce the cost of zero-discharge wastewater treatment in coal-fired power plants.

As a supplement or alternative metering method for physical metering devices, Virtual Flow Metering (VFM) is more and more widely used in the upstream metering scenario of oil and gas fields. To enhance the accuracy of single well VFM system and improve the system maintenance convenience, a new VFM method based on “hybrid model” which combines mechanism model and data model is proposed on the basis of the traditional mechanism model of dynamic multi-phase flow, the interactive logic between machine learning model of big data on single well production condition and mechanism model is established. The new VFM system architecture can realize the stable and accurate operation of the new “hybrid model” VFM system driven by real-time measurement data. The research show that: (1) machine learning model of big data on single well production condition can realize the second index output of three-phase flow under real-time measurement data input; (2) The field data are used to evaluate the accuracy of new VFM results, shows the average errors of liquid flow and gas flow are less than 5% and 3% respectively, and the metering accuracy is improved to a certain extent compared with traditional VFM method; (3) Compared with virtual metering based on the traditional mechanism model, the new VFM system is more convenient in later model maintenance and only adjust few model setting according WC and GOR changing in field data.

Accurate photovoltaic forecasting is crucial for power dispatching, capacity analysis and unit commitment. The existing data-driven prediction algorithm has a certain improvement in calculation speed and prediction accuracy, but fails to consider the internal mechanism of photovoltaic power generation. Besides, there is a risk of generalization. To solve the above problems, a mechanism model was proposed based on Stacking framework and a data-driven prediction model. The photovoltaic generator model was embedded in the Stacking framework layer prediction structure to form a parallel predictive learner based on LSTM ( long short-term memory), XGBoost, and mechanism models. The mechanism model limited photovoltaic power generation to a reasonable range as a prediction constraint of the data-driven model. The intrinsic information was extracted by mechanism model and the analysis of nonlinear relation- ship in historical data is driven by data. Based on the actual data analysis of a certain area in Anhui Province, the proposed model has higher accuracy than the traditional data-driven method. [ABSTRACT FROM AUTHOR]

To solve the problem of online detection of coal-carrying rate in gangue, an approach based on particle queuing and binocular machine vision was proposed. First, a queuing device was applied to line up the coal and gangue particles. A binocular camera was used to obtain images of coal and gangue from above the belt. Through image segmentation, the coal and gangue particle regions were divided from the belt background. Then, the Semi-Global Block Matching (SGBM) algorithm was used to obtain the height information matrix of each region. At the same time, the projected area of the particles was calculated from the area of the divided regions. It was converted from pixel units to physical units by capturing an image of a ruler. The height matrix and projected area were combined to construct a mechanism-based volume model by integral principle. Finally, the calculated volume of coal and gangue were multiplied by their respective empirical density to get the mass of particles. The coal-carrying rate in gangue was obtained by dividing the mass of coal by the total mass. The average relative error of the proposed method is 2.79%. It not only overcomes the prediction error caused by particle stacking, but also improves the computational efficiency of image processing, and the "white-box model" is also easy to understand. [ABSTRACT FROM AUTHOR]

Copyright of Clean Coal Technology is the property of Clean Coal Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design–Build–Test–Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution. [ABSTRACT FROM AUTHOR]

Environmental acidification impairs microorganism diversity and their functions on substance transformation. Rhodococcus is a ubiquitously distributed genus for contaminant detoxification in the environment, and it can also adapt a certain range of pH. This work interpreted the acid responses from both phenotype and metabolism in strain Rhodococcus biphenylivorans TG9T (TG9) induced at pH 3. The phenotype alterations were described with the number of culturable and viable cells, intracellular ATP concentrations, cell shape and entocyte, degradation efficiency of polychlorinated biphenyl (PCB) 31 and biphenyl. The number of culturable cells maintained rather stable within the first 10 days, even though the other phenotypes had noticeable alterations, indicating that TG9 possesses certain capacities to survive under acid stress. The metabolism responses were interpreted based on transcription analyses with four treatments including log phase (LP), acid-induced (PER), early recovery after removing acid (RE) and later recovery (REL). With the overview on the expression regulations among the 4 treatments, the RE sample presented more upregulated and less downregulated genes, suggesting that its metabolism was somehow more active after recovering from acid stress. In addition, the response mechanism was interpreted on 10 individual metabolism pathways mainly covering protein modification, antioxidation, antipermeability, H+ consumption, neutralization and extrusion. Furthermore, the transcription variations were verified with RT-qPCR on 8 genes with 24-hr, 48-hr and 72-hr acid treatment. Taken together, TG9 possesses comprehensive metabolism strategies defending against acid stress. Consequently, a model was built to provide an integrate insight to understand the acid resistance/tolerance metabolisms in microorganisms. [Display omitted] [ABSTRACT FROM AUTHOR]

Energy storage of passive muscles plays an important part in frequent activities of honey bee abdomens due to the muscle distribution and open circulatory system. However, the elastic energy and mechanical properties of structure in passive muscles remain unclear. In this article, stress relaxation tests on passive muscles from the terga of the honey bee abdomens were performed under different concentrations of blebbistatin and motion parameters. In stress relaxation, the load drop with the rapid and slow stages depending on stretching velocity and stretching length reflects the features of myosin–titin series structure and cross-bridge–actin cyclic connections in muscles. Then a model with 2 parallel modules based on the 2 feature structures in muscles was thus developed. The model described the stress relaxation and stretching of passive muscles from honey bee abdomen well for a good fitting in stress relaxation and verification in loading process. In addition, the stiffness change of cross-bridge under different concentrations of blebbistatin is obtained from the model. We derived the elastic deformation of cross-bridge and the partial derivatives of energy expressions on motion parameters from this model, which accorded the experimental results. This model reveals the mechanism of passive muscles from honey bee abdomens suggesting that the temporary energy storage of cross-bridge in terga muscles under abdomen bending provides potential energy for springback during the periodic abdomen bending of honey bee or other arthropod insects. The finding also provides an experimental and theoretical basis for the novel microstructure and material design of bionic muscle. [ABSTRACT FROM AUTHOR]

As a mass and energy transfer process involving microorganisms, the complexity, time variability and severe nonlinearity of anaerobic fermentation process greatly reduced the accuracy and practicability of the prediction models established on pure mechanism or pure data. In order to solve this problem, taking the methane production process of cow dung fermentation as the research object, a hybrid model for predicting gas production by anaerobic fermentation was proposed. Firstly, the clear kinetic equations of hydrolysis, acidification hydrogen production and acetic acid production were established, and then the data-driven model of methane production and liquid-gas mass transfer process was constructed combined with Random Forest Regression(RFR). And the Sparrow Search Algorithm( SSA) was used to optimally estimate the super parameters of RFR algorithm. Finally, two sub-models, the kinetic model of the fermentation mechanism and the random forest regression model, were cascaded to form a hybrid model for the whole process of anaerobic fermentation biogas production prediction. The hybrid model was used to simulate the anaerobic digestion experiment of cow manure. The results showed that the methane production rate using the hybrid model simulation was in good agreement with the experimental data, and the prediction accuracy of the mechanism and data-driven mixed model(SSA-RFR-ADM) was close to the prediction accuracy of the complex pure mechanism model. The mean squared error of the model prediction was 0. 003 035. The proposed hybrid model effectively overcame the limitation of single model, and could be used as a proxy model of complex pure mechanism model, providing correct theoretical guidance for practical anaerobic fermentation methane production process. [ABSTRACT FROM AUTHOR]

The gas flow verification system with positive pressure sonic nozzle is calling for high requirements on the stability of gas source pressure. It is difficult to guarantee the stagnation cabin pressure be controlled within its targeted range, because of varying boundary conditions. To solve the above problems, this paper presents a modeling method based on mechanism and data-driven for gas flow verification system with the positive pressure sonic nozzle. The stochastic configuration network is used to dynamically identify parameters in the mechanism model. After substituting the identification results into the model, the RMSE of the pressure and temperature in the stagnation cabin obtained from the simulation and the actual data are 1.26 × 104 and 2.64 × 104, respectively. Considering the dead zone of the regulating valve and the replacement of regulating valve in the actual operation, the model is analyzed by disturbance experiment. Experimental results show that the dynamic mathematical model could reflect the changing trend of the pressure in the pressure regulating process and prove its effectiveness. [ABSTRACT FROM AUTHOR]

Fuel ethanol has drawn extensive attention as renewable energy. However, modeling the fuel ethanol batch fermentation process is still a critical task. The unstructured kinetic model (UKM) is often utilized to model the process, but it encounters two problems due to the large differences in initial glucose concentrations. First, the UKM has poor predictions of the yeast growth, which is a crucial production index. Second, the kinetic parameters of the UKM are time-varying because of the changing environmental conditions. The constant manually set kinetic parameters affect the prediction accuracy. To tackle the problems, we propose a dynamic hybrid model of the fuel ethanol fermentation process. First, a biomass concentration prediction model based on extreme gradient boosting is developed. It predicts the values of biomass concentrations and mycelium growth rate as supplementary mechanism knowledge. Then, we present an artificial neural network-based model to determine the time-varying kinetic parameters. Our model can accurately predict the time series of biomass, ethanol, and glucose concentrations, with RMSEs reaching 0.3323, 1.9295, and 3.0540. Experimental results show that the dynamic hybrid model performs with satisfactory accuracy in modeling the fuel ethanol fermentation process. [ABSTRACT FROM AUTHOR]

Building the physics-driven mechanism model has always been the core scientific paradigm for parameter estimation in Earth surface systems, and developing the data-driven machine learning model is a crucial way for paradigm transformation in geoscience research. The coupling of mechanism and learning models can realize the combination of "rationalism" and "empiricism", which is one of the most concerned research hotspots. In this paper, for remote sensing inversion and dynamic simulation, we deeply analyze the internal bottleneck and complementarity of mechanism and learning models and build a coupling paradigm framework with mechanism-learning cascading model, learning-embedded mechanism model, and mechanism-infused learning model. We systematically summarize ten specific coupling methods, including preprocessing and initialization, intermediate variable transfer, post-refinement processing, model substitution, model adjustment, model solution, input variable constraints, objective function constraints, model structure constraints, hybrid, etc., and analyze the main existing problems and future challenges. The research aims to provide a new perspective for in-depth understanding and application of the mechanism-learning coupling model and provide theoretical and technical support for improving the inversion and simulation capabilities of parameters in Earth surface systems and serving the development of Earth system science. [ABSTRACT FROM AUTHOR]

Water and pyrite (FeS2) in coal have great synergistic effect on coal self-ignite. In order to explore the characteristics of spontaneous combustion of high humidity and high sulfur coal, this paper selected different concentrations of water and pyrite to carry out thermogravimetric experiment and oxidation reaction kinetics analysis. The changes of coal samples oxidation under low temperature in mass and characteristic temperatures were studied through thermogravimetric (TG) analysis. The results indicate that the promoting effect of water on coal spontaneous combustion turns to restrain when it reaches 15%, and water and pyrite have substantial synergistic effect on coal oxygen absorption and mass gain stage (T3–T5 in TG temperatures), besides there is a range of water/pyrite contents (10–15 mass% water and 2–4 mass% pyrite) that have the largest effect on coal oxidation reactions. Through the analysis of oxidation reaction kinetics, it was found that the change rule of the apparent activation energy (Ea) in coal oxygen absorption and mass gain stage is similar to that of the thermogravimetric change, and it was the lowest at 2–4 mass% pyrite and 10–15 mass% water content. The reaction process of coal oxidation by water and pyrite conforms to the equation n = 4 Avrami-Erofeev, therefore water and pyrite just can promote coal oxidation externally. This research provides support for further understanding and control the influence of water and associated pyrite on coal self-ignite. [ABSTRACT FROM AUTHOR]

In order to study the explosion of micro‐sized and nanosized iron powder, the explosion overpressure and flame propagation were measured in a 20‐L near‐spherical explosion pressure experiment and a Hartmann tube experiment. The explosion overpressure experiments show that the optimum explosion concentrations of micro‐sized and nanosized iron powder are 750 and 375 g/m3, respectively. The maximum explosion pressure and the maximum explosion pressure rise rate of nanosized iron powder are 0.56 MPa and 46.41 MPa/s, which are 144% and 225% of that of micro‐sized iron powder, respectively. Under the optimum explosion concentration, the explosion flame of nanosized iron powder is brighter, and the propagation time is shorter. The maximum propagation velocity of micro‐sized and nanosized iron powder flame is 0.84 and 6.55 m/s, respectively, and the flame pulsation degree of micro‐sized iron powder is larger. The spherical particles and the cracks on the surface of the product particles were observed in the micro images of the explosion products, and the explosion mechanism model of micro‐sized and nanosized iron powder was obtained. It was observed that the deformation of micro‐sized iron particles intensifies, forming regular and complete spherical iron oxide particles. [ABSTRACT FROM AUTHOR]

The goal and task of intelligent fully mechanized working face are to independently complete the reliable coal cutting of the fully mechanized working face, maintain the geometric relationship of the working face and reliable roof support. According to the goal and task, the key technologies of intelligent control of fully mechanized working face are proposed. The technologies include shearer positioning technology, working face visualization technology, hydraulic support electro-hydraulic control technology (device), working face communication technology, collaborative control technology of fully mechanized mining equipment, autonomous height adjustment technology of shearer, autonomous straightening technology of working face and surrounding rock support control technology of working face. Among these technologies, the first three technologies belong to the perception and execution layer of intelligent fully mechanized working face. The working face communication technology is the transmission layer of intelligent fully mechanized working face. And the last four technologies belong to the decision-making layer of intelligent fully mechanized working face. The challenges faced by the intelligent fully mechanized working face are pointed out, which are that the autonomous decision-making capability of the decision-making layer cannot adapt to the complex and changeable working conditions, and the perception and execution layer cannot support the information demand of the decision-making layer and the reliable execution of the decision-making instructions. In order to solve the above challenges, the digital twin system architecture of fully mechanized working face is proposed by use of the simulation-based digital twin modeling method. The virtual entity of the digital twin system of the fully mechanized working face comprises a mechanism model and a behavior model. The unmeasurable data of a physical system of the fully mechanized working face equipment can be obtained by the mechanism model. The behavior model can provide holographic information reflecting the running state of the physical equipment for an intelligent control system of the fully mechanized working face. Thus the problem of the lack of data information in the decision-making layer is solved. The off-line run mode of the combination of the mechanism model of fully mechanized mining equipment and its control system forms the hardware in the loop simulation system of fully mechanized working face, which provides a test platform for intelligent control algorithms based on process rules. The off-line run mode of the combination of the mechanism model, behavior model and its control system of the fully mechanized mining equipment forms the calculation experimental system of the fully mechanized working face, which provides a test platform for the development of the real independent decision-making complex algorithm of the intelligent control system of the fully mechanized working face.

Abstract Initial residual stress is the main reason causing machining deformation of the workpiece, which has been deemed as one of the most important aspects of machining quality issues. The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control. Due to the principle error of existing residual stress detection methods, there are still challenges in practical applications. Aiming at the detection problem of the initial residual stress field, an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper. Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress, and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish; the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model. Finally, the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model. Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

In order to improve the leaching efficiency of gold ore and reduce the environmental treatment cost of residual sodium cyanide, continuous stirred tank reactors are often connected in a cascade manner. A gold leaching system is a multiphase chemical reaction system, and its kinetic reaction mechanism is complex and affected by random factors. Using intelligent modeling technology to establish a hybrid prediction model of the leaching system, the dynamic performance of the process can be easily analyzed. According to the reaction principle and the theory of substance conservation, a mechanism model is established to reflect the main dynamic performance of the leaching system. In order to improve the global convergence of the optimization target, a particle swarm optimization (PSO) algorithm based on simulated annealing is used to optimize the adjustment parameters in the kinetic reaction velocity model. The multilayer long short-term memory (LSTM) neural network approach is used to compensate for the prediction errors caused by the unmodeled dynamics, and a hybrid model is established. The hybrid prediction model can accurately predict the leaching rate, which provides a reliable basis for guiding production, and also provides a model basis for process optimization, controller design, and operation monitoring. Finally, the superiority and practicability of the hybrid model are verified by a practical leaching industrial system test. The prediction model of key variables in the leaching process is established for the first time using the latest time series prediction technology and intelligent optimization technology. The research results of this paper can provide a good reference and guidance for other research on complex system hybrid modeling. [ABSTRACT FROM AUTHOR]

Aiming to investigate the problem that dam-monitoring data are difficult to analyze in a timely and accurate automated manner, in this paper, we propose an automated framework for dam health monitoring based on data microservices. The framework consists of structural components, monitoring sensors, and a digital virtual model, which is a hybrid of a finite element (FE) model, a geometric model, a mathematical model, and a deep learning algorithm. Long short-term memory (LSTM) was employed to accurately fit and predict the monitoring data, while dynamic inversion and simulation were used to calibrate and update the data in the hybrid model. The automated tool enables systematic maintenance and management, minimizing errors that are commonly associated with manual visual inspections of structures. The effectiveness of the framework was successfully validated in the safety monitoring and management of a practical dam project, in which the hybrid model improved the prediction accuracy of monitored data, with a maximum absolute error of 0.35 mm. The proposed method can be considered user-friendly and cost-effective, which improves the operational and maintenance efficiency of the project with practical significance.

Aeroengine performance diagnosis technology is essential for ensuring flight safety and reliability. The complexity of engine performance and the strong coupling of fault characteristics make it challenging to develop accurate and efficient gas path diagnosis methods. To address these issues, this study proposes a novel digital twin framework for aeroengines that achieves the digitalization of physical systems. The mechanism model is constructed at the component level. The data-driven model is built using a particle swarm optimization–extreme gradient boosting algorithm (PSO-XGBoost). These two models are fused using the low-rank multimodal fusion method (LWF) and combined with the sparse stacked autoencoder (SSAE) to form a digital twin framework of the engine for performance diagnosis. Compared to methods that are solely based on mechanism or data, the proposed digital twin framework can effectively use mechanism and data information to improve the accuracy and reliability. The research results show that the proposed digital twin framework has an error rate of 0.125% in predicting gas path parameters and has a gas path fault diagnosis accuracy of 98.6%. Considering that the degradation cost of a typical flight mission for only one aircraft engine after 3000 flight cycles is approximately USD 209.5, the proposed method has good economic efficiency. This framework can be used to improve engine reliability, availability, and efficiency, and has significant value in engineering applications.

The feasibility of sulfur enhancement for uranium bioleaching in column reactors was assessed with a designed mixed Acidithiobacillus ferrooxidans, Acidithiobacillus thiooxidans and Leptospirillum ferriphilum from a refractory uranium ore. The uranium extraction reached 86.2% with the sulfur enhancement (1 g/kg) in 77 days leaching process, increased by 12.6% vs. the control without sulfur addition. The kinetic analysis showed that uranium bioleaching with sulfur enhancement in columns followed an internal diffusion through the product layer-controlled model. Ore residue characteristics indicated that sulfur enhancement could strengthen the porosity of passivation layer, improving the ore permeability. Notably, bacterial community analysis showed that sulfur enhancement at 1 g/kg could make the iron-oxidizing and sulfur-oxidizing bacteria on the ore surface maintain a good balance (approx. 1:1), and thus decomposing ore more effectively. Lastly, a possible mechanism model for uranium bioleaching with sulfur enhancement was proposed.

The mapping of tropical rainforest forest structure parameters plays an important role in biodiversity and carbon stock estimation. The current mechanism models based on PolInSAR for forest height inversion (e.g., the RVoG model) are physical process models, and realistic conditions for model parameterization are often difficult to establish for practical applications, resulting in large forest height estimation errors. As an alternative, machine learning approaches offer the benefit of model simplicity, but these tools provide limited capabilities for interpretation and generalization. To explore the forest height estimation method combining the mechanism model and the empirical model, we utilized UAVSAR multi-baseline PolInSAR L-band data from the AfriSAR project and propose a solution of a mechanism model combined with machine learning. In this paper, two mechanism models were used as controls, the RVoG three-phase method and the RVoG phase-coherence amplitude method. The vertical structure parameters of the forest obtained from the mechanism model were used as the independent variables of the machine learning model. Random forest (RF) and partial least squares (PLS) regression models were used to invert the forest canopy height. Results show that the inversion accuracy of the machine learning method, combined with the mechanism model, is significantly better than that of the single-mechanism model method. The most influential independent variables were penetration depth, volume coherence phase center height, coherence separation, and baseline selection. With the precondition that the cumulative contribution of the independent variables was greater than 90%, the number of independent variables in the two study areas was reduced from 19 to 4, and the accuracy of the RF-RVoG-DEP model was higher than that of the PLS-RVoG-DEP model. For the Lope test area, the R2 of the RVoG phase coherence amplitude method is 0.723, the RMSE is 8.583 m, and the model bias is −2.431 m; the R2 of the RVoG three-stage method is 0.775, the RMSE is 7.748, and the bias is 1.120 m, the R2 of the PLS-RVoG-DEP model is 0.850, the RMSE is 6.320 m, and the bias is 0.002 m; and the R2 of the RF-RVoG-DEP model is 0.900, the RMSE is 5.154 m, and the bias is −0.061 m. The results for the Pongara test area are consistent with the pattern for the Lope test area. The combined "fusion model" offers a substantial improvement in forest height estimation from the traditional mechanism modeling method. [ABSTRACT FROM AUTHOR]

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To enhance the stable performance of wind farm (WF) equivalent models in uncertain operating scenarios, a model-data-driven equivalent modeling method for doubly-fed induction generator (DFIG)-based WFs is proposed. Firstly, the aggregation-based WF equivalent models and the equivalent methods for aggregated parameters are analyzed and compared. Two mechanism models are selected from the perspective of practicality and complementarity of simulation accuracy. Secondly, the simulation parameters are set through two sampling methods to construct a training database. Next, the whole fault process is divided into five phases, and the weight coefficient optimization model is established according to the data-driven idea to achieve the adaptive configuration of the weight. Finally, the electromechanical transient simulations of the power systems with a DFIG-based WF is carried out by using the MATLAB/Simulink platform. Compared with the detailed WF model, the simulation time of the WF equivalent proposed in this paper can be significantly reduced by about 87%, and simulation results show that the proposed method can effectively improve the adaptability of the WF equivalent model in different wind scenarios and voltage dips. [ABSTRACT FROM AUTHOR]

The assembly, as the core part of low-pressure rotor manufacturing, is of great importance to ensure its unbalance. Low-voltage rotor assembly is a multi-process process influenced by the quality of part machining, assembly process, and assembly quality, resulting in unbalance that is difficult to predict during the assembly process. The unbalance measurement in the assembly process is important for the subsequent process optimization. Therefore, in order to achieve the prediction of unbalance measurement in the assembly process, this paper proposes an unbalance measurement prediction method based on mechanism and data fusion. Firstly, through research and analysis, the influencing factors of unbalance are determined, the low-pressure rotor blade sequencing mechanism model is established, and the blade sequencing optimization is realized by using reinforcement learning. Then, since the unbalance is formed after all the processes are completed and the subsequent work steps in the assembly process have not been carried out yet, the actual process parameters cannot be obtained, the semi-physical simulation method is used to combine the actual data of the assembled work steps with the theoretical data of the unassembled work steps to build a prediction model of the unbalance based on the BRNN (bidirectional recurrent neural network) network to achieve the prediction of the unbalance measurement in the assembly process. Finally, the model was validated using actual assembly process data, which proved the feasibility and effectiveness of the method. [ABSTRACT FROM AUTHOR]

This essay reviews the discipline-connecting potential of experimentation. Two examples are used to illustrate how researchers in the first half of the twentieth century profitably combined resources from different disciplines in their experiments. These experiments were designed to test mechanism models describing chemical processes underlying the behavior of biological systems. The researchers had clear expectations about how certain interventions should affect the behavior of the organisms studied, if that behavior was indeed based on the presumed chemical processes. They manipulated the organisms in the relevant ways and determined how the behavior of the organisms changed as a result. [ABSTRACT FROM AUTHOR]

Theoretical research has explained the process of dioxin (DXN) formation in the municipal solid waste incineration (MSWI). This process includes the generation, adsorption, and emission of DXN. Actual DXN concentrations often significantly deviate from theoretical models. This discrepancy is influenced by several key factors: the type of integrated municipal solid waste (MSW) treatment process, the characteristics of the waste, and the operational controls. The progression of DXN generation, adsorption, and emission concentrations within the MSWI process remains unclear. This lack of clarity is especially pronounced when examining the accounting for the specific components of the MSW. To unravel the evolution of DXN, this article proposes a comprehensive numerical simulation model for the entire process of DXN concentration in an MSWI plant. The model is designed based on existing knowledge of MSW combustion and DXN mechanisms, leveraging FLIC and ASPEN simulation software. It incorporates six key stages to facilitate the DXN simulation: precipitation and formation, high-temperature pyrolysis, high-temperature gas-phase synthesis, low-temperature catalytic synthesis, adsorption on activated carbon, and emission to the atmosphere. Under both benchmark and multiple operating conditions, the simulated experiments confirm the effective representation of the evolution of DXN concentrations throughout the process. Consequently, this study presents a model designed to enhance the development of strategies aimed at reducing DXN emissions and to foster innovation in intelligent control technologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Large oil and gas pipeline networks are characterized by multiple source and sink nodes, large spatial span and high degree of coupling between the thermal and hydraulic processes, which lead to great difficulty in modeling. It is pointed out in the Medium and long-term oil and gas pipeline network planning that the intellectualization of pipeline networks is the development direction in the future, and building a hybrid model with clear physical meaning and strong generalization capabilities by combining the mechanism knowledge and the data-driven modeling method is critical to realize the intelligent pipeline networks. Herein, the characteristics of mechanism modeling and data-driven modeling were analyzed, the physical properties of the study object were described collaboratively by integrating the mechanism model and the datadriven model, the internal connection among the site data was fully mined, the evolution laws of the process variations were explored, and finally a high-fidelity hybrid model was established. In addition, various structures of hybrid models and the feasibility of their application in oil and gas pipeline industry was summarized, the strategies of hybrid modeling in various application scenarios were clarified and the direction of research on the mechanism and data based collaborative modeling technology in future was discussed. Further, the research results can provide reference to the construction of intelligent pipeline networks.