Quantitative prediction process and evaluation method for seafloor polymetallic sulfide resources

Seafloor polymetallic sulfide resources exhibit significant development potential. In 2011, China received the exploration rights for 10,000 km2 of a polymetallic sulfides area in the Southwest Indian Ocean; China will be permitted to retain only 25% of the area in 2021. However, an exploration of seafioor hydrothermal sulfide deposits in China remains in the initial stage. According to the quantitative prediction theory and the exploration status of seafloor sulfides, this paper systematically proposes a quantitative prediction evaluation process of oceanic polymetallic sulfide resources and divides it into three stages： prediction in a large area, prediction in the prospecting region, and the verification and evaluation of targets. The first two stages of the prediction process have been employed in seafloor sulfides prospecting of the Chinese contract area. The results of stage one suggest that the Chinese contract area is located in the high posterior probability area, which indicates good prospecting potential area in the Indian Ocean. In stage two, the Chinese contract area of 48^-52~E has the highest posterior probability value, which can be selected as the reserved region for additional exploration. In stage three, the method of numerical simulation is employed to reproduce the ore-forming process of sulfides to verify the accuracy of the reserved targets obtained from the three-stage prediction. By narrowing the exploration area and gradually improving the exploration accuracy, the prediction will provide a basis for the exploration and exploitation of seafloor polymetallic sulfide resources.

Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

Improved Quality Prediction Model for Multistage Machining Process Based on Geometric Constraint Equation

Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process（MMP）. However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.

A hybrid spatial-temporal deep learning prediction model of industrial methanol-to-olefins process

Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully industrialized.The accurate prediction of process variables can yield significant benefits for advanced process control and optimization.The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes,such as high nonlinearities,dynamics,and data distribution shift caused by diverse operating conditions.In this paper,we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues.Firstly,a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions.Subsequently,convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns.Meanwhile,a multi-graph convolutional network is leveraged to model the spatial interactions.Afterward,the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction.Finally,the outputs are denormalized to obtain the ultimate results.The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices,making the model more interpretable.Lastly,this model is deployed onto an end-to-end Industrial Internet Platform,which achieves effective practical results.

Two-stage reproduction derived from cells of thallus could directly contribute to seeds for green tidal algal Enteromorpha(Ulva) prolifera/clathrata bloom,with disclosure of their ephemeral trait

Green tidal algal Enteromorpha species complete their life cycles by the isomorphic alternation of generations.The provenance of green tide caused by them in the western Yellow Sea has been disputed. The cell reproduction derived from adult thallus was observed on E. clathrata collected from Shantou City, Guangdong Province in this study. Subsequently, it further found that E. proliferia collected from Qingdao City, Shandong Province and Qinhuangdao City, Hebei Province, produced reproductive cells by somatic cells of its early infantile thallus or branch. The latter is functionally similar to that the seedlings of red alga Porphyra yezoensis produce the monospores, and could exquisitely explain the ephemeral or opportunistic trait and environmental adaptation ability of Enteromorpha species. Changes in growth conditions may induce the two types of cell reproduction.They contribute to the bloom, and can effectively reveal the seasonally occurring large-scale and on-year and offyear phenomenon. The latter may have played a decisive role in its formation. This paper analyses the legal status of the species name, the type of generation during bloom, ephemeral traits, the role of microscopic propagule, the area of origin, on-year and off-year phenomenon, early warning and prevention and control of the species, and so on. On this basis, further study on the influence of environmental factors on cell reproduction of early infantile thalli or branches will achieve a positive effect for early warning and prevention and control of the green tidal algal bloom.

Quantitative design of yield components to simulate yield formation for maize in China

Maize(Zea mays L.) stands prominently as one of the major cereal crops in China as well as in the rest of the world.Therefore,predicting the growth and yield of maize for large areas through yield components under high-yielding environments will help in understanding the process of yield formation and yield potential under different environmental conditions.This accurate early assessment of yield requires accuracy in the formation process of yield components as well.In order to formulate the quantitative design for high yields of maize in China,yield performance parameters of quantitative design for high grain yields were evaluated in this study,by utilizing the yield performance equation with normalization of planting density.Planting density was evaluated by parameters including the maximum leaf area index and the maximum leaf area per plant.Results showed that the variation of the maximum leaf area per plant with varying plant density conformed to the Reciprocal Model,which proved to have excellent prediction with root mean square error(RMSE) value of 5.95%.Yield model estimation depicted that the best optimal maximum leaf area per plant was 0.63 times the potential maximum leaf area per plant of hybrids.Yield performance parameters for different yield levels were quantitatively designed based on the yield performance equation.Through validation of the yield performance model by simulating high yields of spring maize in the Inner Mongolia Autonomous Region and Jilin Province,China,and summer maize in Shandong Province,the yield performance equation showed excellent prediction with the satisfactory mean RMSE value(7.72%) of all the parameters.The present study provides theoretical support for the formulation of quantitative design for sustainable high yield of maize in China,through consideration of planting density normalization in the yield prediction process,providing there is no water and nutrient limitation.

Acknowledgments

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A progressive approach to predict shot peening process parameters for forming integral panel of Al7050-T7451

In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flow,peening distance and peening angle are determined according to the empirical and machine type.Then,the optimal value of air pressure for the whole shot peening is selected by the experimental data.Finally,the feeding speed for every shot peening path is predicted by regression equation.The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object,and four experiments are conducted.In order to design specimens for acquiring the forming data,one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens,which were peened along the middle of the two stringers.The most striking finding of this experiment is that the outer shape error range is below 3.9%,so the plate specimens can be used in predicting feeding speed of the integral panel.The second experiment is performed and results show that when the coverage reaches the limit of 80%,the minimum feeding speed is 50 mm/s.By this feeding speed,the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object,and the optimal air pressure is 0.15 MPa.Then,the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment,and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method.The algorithm is validated by forming a test part and the average deviation is 0.496 mm.It is shown that the approach can realize the forming of the integral panel precisely.

Effect of thermo-mechanical processing parameters on the dynamic restoration mechanism in an Mg-4Y-2Nd-1Sm-0.5Zr alloy during hot compression

Microstructure evolution and dynamic restoration mechanism of solution-treated Mg-4Y-2Nd-1Sm-0.5Zr alloy have been studied under three TMP parameters consisting of deformation temperatures(350-500℃),strain rates(0.01-5 s^(-1)),and strains(0.2,0.4,and 0.8).A strong dynamic softening is observed in all stress-strain curves,even at higher strain rates(1 and 5 s^(-1))due to an adiabatic heating effect.Various stress-strain curves are applied to construct a processing map and develop an Arrhenius-type constitutive equation.With the prediction of the processing map,an optimal processing domain has been determined to be the temperature range 450-500℃and strain rate range 0.01-0.1 s^(-1)at a strain of 0.8.The volume fraction of DRX grains is the largest in the corresponding domain of high temperature and low strain rate.For the effect of TMP parameters on the dynamic restoration,the discontinuous dynamic recrystallization(DDRX)and continuous DRX(CDRX)synergistic effect occur throughout the whole process at high temperature and high strain rate.In terms of high temperature and low strain rate,DDRX characteristics at a low strain and then the DDRX+CDRX synergistic effect is observed at a higher strain.Although the DRX process is weak at low temperature and low strain rate,deformation twins have occurred and provided nucleation sites for DRX grains.

Integration of solar thermal and photovoltaic, wind, and battery energy storage through AI in NEOM city

NEOM is a“New Future”city powered by renewable energy only,where solar photovoltaic,wind,solar ther-mal,and battery energy storage will supply all the energy needed to match the demand integrated by artificial intelligence techniques.Within this context,the weight of solar thermal is supposed to increase.Concentrated solar power is the only renewable energy with the added value of dispatchability.Opposite to solar photovoltaic and wind,which suffer from intermittency and unpredictability,thus necessitating economically and environ-mentally expensive external energy storage by batteries,concentrated solar power may be fitted with internal energy storage by molten salt providing a much cheaper and environmentally friendly alternative.Oversizing the solar field and the thermal energy storage,the otherwise traditional design with steam Rankine cycles of temperature and pressure to turbine about 565℃ and 100 bar permits highly dispatchable electricity with Lev-elized Cost of Electricity(LCOE)slightly above 7.5¢/kWh in NEOM City,Kingdom of Saudi Arabia.By using higher temperature and pressure to the turbine of 730℃ and 330 bar,the LCOE can be further reduced to below 6.5¢/kWh.While wind and solar photovoltaic are much cheaper,at less than 3–4¢/kWh,their intermittency and unpredictability necessitate energy storage by Lithium-Ion batteries of additional cost 14–28¢/kWh.Likely,the integration of renewable energy technologies through Artificial Intelligence(AI)will be the New Future in NEOM City,with solar photovoltaic,wind,battery energy storage,and solar thermal,the building blocks,and solar thermal increasing the share of energy supply.