Model Prediction and Optimal Control of Gas Oxygen Content for A Municipal Solid Waste Incineration Process

In the municipal solid waste incineration process,it is difficult to effectively control the gas oxygen content by setting the air flow according to artificial experience.To address this problem,this paper proposes an optimization control method of gas oxygen content based on model predictive control.First,a stochastic configuration network is utilized to establish a prediction model of gas oxygen content.Second,an improved differential evolution algorithm that is based on parameter adaptive and t-distribution strategy is employed to address the set value of air flow.Finally,model predictive control is combined with the event triggering strategy to reduce the amount of computation and the controller's frequent actions.The experimental results show that the optimization control method proposed in this paper obtains a smaller degree of fluctuation in the air flow set value,which can ensure the tracking control performance of the gas oxygen content while reducing the amount of calculation.

Nitrogen Content Inversion of Corn Leaf Data Based on Deep Neural Network Model

To obtain excellent regression results under the condition of small sample hyperspectral data,a deep neural network with simulated annealing(SA-DNN)is proposed.According to the characteristics of data,the attention mechanism was applied to make the network pay more attention to effective features,thereby improving the operating efficiency.By introducing an improved activation function,the data correlation was reduced based on increasing the operation rate,and the problem of over-fitting was alleviated.By introducing simulated annealing,the network chose the optimal learning rate by itself,which avoided falling into the local optimum to the greatest extent.To evaluate the performance of the SA-DNN,the coefficient of determination(R^(2)),root mean square error(RMSE),and other metrics were used to evaluate the model.The results show that the performance of the SA-DNN is significantly better than other traditional methods.

Modelling the dead fuel moisture content in a grassland of Ergun City,China

The dead fuel moisture content(DFMC)is the key driver leading to fire occurrence.Accurately estimating the DFMC could help identify locations facing fire risks,prioritise areas for fire monitoring,and facilitate timely deployment of fire-suppression resources.In this study,the DFMC and environmental variables,including air temperature,relative humidity,wind speed,solar radiation,rainfall,atmospheric pressure,soil temperature,and soil humidity,were simultaneously measured in a grassland of Ergun City,Inner Mongolia Autonomous Region of China in 2021.We chose three regression models,i.e.,random forest(RF)model,extreme gradient boosting(XGB)model,and boosted regression tree(BRT)model,to model the seasonal DFMC according to the data collected.To ensure accuracy,we added time-lag variables of 3 d to the models.The results showed that the RF model had the best fitting effect with an R2value of 0.847 and a prediction accuracy with a mean absolute error score of 4.764%among the three models.The accuracies of the models in spring and autumn were higher than those in the other two seasons.In addition,different seasons had different key influencing factors,and the degree of influence of these factors on the DFMC changed with time lags.Moreover,time-lag variables within 44 h clearly improved the fitting effect and prediction accuracy,indicating that environmental conditions within approximately 48 h greatly influence the DFMC.This study highlights the importance of considering 48 h time-lagged variables when predicting the DFMC of grassland fuels and mapping grassland fire risks based on the DFMC to help locate high-priority areas for grassland fire monitoring and prevention.

Quantitative characterizations of anisotropic dynamic properties in organic-rich shale with different kerogen content

Understanding the quantitative responses of anisotropic dynamic properties in organic-rich shale with different kerogen content(KC)is of great significance in hydrocarbon exploration and development.Conducting controlled experiments with a single variable is challenging for natural shales due to their high variations in components,diagenesis conditions,or pore fluid.We employed the hot-pressing technique to construct 11 well-controlled artificial shale with varying KC.These artificial shale samples were successive machined into prismatic shape for ultrasonic measurements along different directions.Observations revealed bedding perpendicular P-wave velocities are more sensitive to the increasing KC than bedding paralleling velocities due to the preferential alignments of kerogen.All elastic stiffnesses except C_(13)are generally decreasing with the increasing KC,the variation of C_(1) and C_(33)on kerogen content are more sensitive than those of C_(44)and C_(66).Apparent dynamic mechanical parameters(v and E)were found to have linear correlation with the true ones from complete anisotropic equations independent of KC,which hold value towards the interpretation of well logs consistently across formations,Anisotropic mechanical parameters(ΔE and brittlenessΔB)tend to decrease with the reducing KC,withΔB showing great sensitivity to KC variations.In the range of low KC(<10%),the V_(P)/V_(S) ratio demonstrated a linearly negative correlation with KC,and the V_(P)/V_(S) ratio magnitude of less than 1.75may serve as a significant characterization for highly organic-rich(>10%)shale,compilation of data from natural organic rich-shales globally verified the similar systematic relationships that can be empirically used to predict the fraction of KC in shales.

A new shear rheological model for a soft interlayer with varying water content

The rheological behavior of a soft interlayer is critical to understanding slope stability, which is closely related to the water content of the soft interlayer. This study used the soft interlayer of the Permian Maokou Formation in Southwest China as an example to perform ring shear creep tests with different water content amounts. The effect of water content on the creep properties of the soft interlayer was analyzed, and a new shear rheological model was established. This research produced several findings. First, the ring shear creep deformation of the soft interlayer samples varied with the water content and the maximum instantaneous shear strain increment occurred near the saturated water content. As the water content increased, the cumulative creep increment of the samples increased. Second, the water content significantly affected the long-term strength of the soft interlayer, which decreased with the increase of water content, exhibiting a negative linear correlation. Third, a constitutive equation for the new rheological model was derived, and through fitting of the ring shear creep test data, the validity and applicability of the constitutive equation were proven. This study has developed an important foundation for studying the long-term deformation characteristics of a soft interlayer with varying water content.

Nutrient Application Model Affects the Contents of Chlorophyll and Carotenoid in Functional Leaves of Early Rice

The objective of this study was to investigate the effects of different nutri-ent application models on the contents of chlorophyl and carotenoid in the functional leaves of early rice. Using rice cultivar Xiangzaoxian45 as experimental materials, the experiment was performed by designing 6 treatments, i.e., T1 （fertilization without nitrogen）, T2（local conventional fertilization）, T3（fertilization for high yield and high effi-ciency）, T4 （fertilization for super high yield）, T5 （fertilization application for super high yield and high efficiency A） and T6 （fertilization application for super high yield and high efficiency B） in two experimental plots Yiyang and Xiangyin. The results showed that T3 respectively increased the contents of chlorophyl and carotenoid at fil ing stage by 29.27%, 38.20% and 13.16%, 30.12% in Yiyang and Xiangyin, as wel as yield of early rice by 4.20%, 4.80% to T2 on the condition of saving 20% ni-trogen fertilizer. Additional y, T5 and T6 on the condition of saving 16.7% nitrogen fertilizer by T4 increased the contents of chlorophyl and carotenoid of fil ing stage by 53.91%, 53.73% and 35.95%, 37.47% in Yiyang and Xiangyin, as wel as yield of early rice by 16.60%, 18.75% to T2 in Yiyang; increased the contents of chlorophyl and carotenoid at fil ing stage by 57.82%, 56.80% and 54.88%, 57.03% in Yiyang and Xiangyin, as wel as yield of early rice 10.10%, 6.75% to T2 in Xiangyin. More-over, there was a significant correlation or an extremely significant correlation be-tween yield and the contents of chlorophyl and carotenoid at different soil fertility level （P〈0.05 or P〈0.01）. Therefore, nutrient application plays an important role in the contents of chlorophyl and carotenoid in the functional leaves of early rice.

Prediction model of moisture content of dead fine fuel in forest plantations on Maoer Mountain,Northeast China

Preventing and suppressing forest fires is one of the main tasks of forestry agencies to reduce resource loss and requires a thorough understanding of the importance of factors affecting their occurrence.This study was carried out in forest plantations on Maoer Mountain in order to develop models for predicting the moisture content of dead fine fuel using meteorological and soil variables.Models by Nelson(Can J For Res 14:597-600,1984)and Van Wagner and Pickett(Can For Service 33,1985)describing the equilibrium moisture content as a function of relative humidity and temperature were evaluated.A random forest and generalized additive models were built to select the most important meteorological variables affecting fuel moisture content.Nelson’s(Can J For Res 14:597-600,1984)model was accurate for Pinus koraiensis,Pinus sylvestris,Larix gmelinii and mixed Larix gmelinii—Ulmus propinqua fuels.The random forest model showed that temperature and relative humidity were the most important factors affecting fuel moisture content.The generalized additive regression model showed that temperature,relative humidity and rain were the main drivers affecting fuel moisture content.In addition to the combined effects of temperature,rainfall and relative humidity,solar radiation or wind speed were also significant on some sites.In P.koraiensis and P.sylvestris plantations,where soil parameters were measured,rain,soil moisture and temperature were the main factors of fuel moisture content.The accuracies of the random forest model and generalized additive model were similar,however,the random forest model was more accurate but underestimated the effect of rain on fuel moisture.

Study on Model of Correlation Between Chemical Ele－ment Contents in Tree Rings and Soils near Tree Roots

The chemical element contents in tree rings are correlated with those in the soils near the tree roots. Theresults in the present study showed that the correlation between them could be described using the followinglogarithmic linear correlation model:lgC'(Z) = α(Z) + b(Z)lgC(Z).Therefore, by determining the chrono-sequence C(Z, t), where Z is the atomic number and t the year ofelemental contents in the annual growth rings of trees, we could get the chrono-sequence C'(Z, t) of elementalcontents in the soil, thus inferring the dynamic variations of relevant elemental contents in the soil.

Diurnal variation models for fine fuel moisture content in boreal forests in China

Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.

Quantitative inverse modeling of nitrogen content from hyperion data under stress of exhausted coal mining sites

Monitoring and evaluating the nutritional status of vegetation under stress from exhausted coal mining sites by hyper-spectral remote sensing is important in future ecological restoration engineering. The Wangpingcun coal mine, located in the Mentougou district of Beijing, was chosen as a case study. The ecological damage was analyzed by 3S technology, field investigation and from chemical data. The derivative spectra of the diagnostic absorption bands are derived from the spectra measured in the field and used as characteristic spectral variables. A correlation analysis was conducted for the nitrogen content of the vegetation samples and the fast derivative spectrum and the estimation model of nitrogen content established by a multiple stepwise linear regression method. The spatial distribution of nitrogen content was extracted by a parameter mapping method from the Hyperion data which revealed the distribution of the nitrogen content. In addition, the estimation model was evaluated for two evaluation indicators which are important for the precision of the model. Experimental results indicate that by linear regression and parameter mapping, the estimation model precision was Very high. The coefficient of determination, R2, was 0.795 and the standard deviation of residual （SDR） 0.19. The nitrogen content of most samples was about 1.03% and the nitrogen content in the study site seems inversely proportional to the distance from the piles of coal waste. Therefore, we can conclude that inversely modeling nitrogen content by hyper-spectral remote sensing in exhausted coal mining sites is feasible and our study can be taken as reference in species selection and in subseauent management and maintenance in ecological restoration.

MIMO Soft-sensor Model of Nutrient Content for Compound Fertil- izer Based on Hybrid Modeling Technique

In compound fertilizer production, several quality variables need to be monitored and controlled simultaneously. It is very diifficult to measure these variables on-line by existing instruments and sensors. So, soft-sensor technique becomes an indispensable method to implement real-time quality control. In this article, a new model of multi-inputs multi-outputs （MIMO） soft-sensor, which is constructed based on hybrid modeling technique, is proposed for these interactional variables. Data-driven modeling method and simplified first principle modelingmethod are combined in this model. Data-driven modeling method based on limited memory partial least squares（LM-PLS） al.gorithm is used to build soft-senor models for some secondary variables.then, the simplified first principle model is used to compute three primary variables on line. The proposed model has been used in practicalprocess; the results indicate that the proposed model is precise and efficient, and it is possible to realize on line quality control for compound fertilizer process.

Theoretical modeling of the effects of temperature and moisture content on the acoustic velocity of Pinus resinosa wood

To investigate the effects of temperature and moisture content(MC) on acoustic wave velocity(AWV)in wood,the relationships between wood temperature,MC,and AWV were theoretically analyzed.According to the theoretical propagation characteristics of the acoustic waves in the wood mixture and the differences in velocity among various media(including ice,water,pure wood or oven-dried wood),theoretical relationships of temperature,MC,and AWV were established,assuming that the samples in question were composed of a simple mixture of wood and water or of wood and ice.Using the theoretical model,the phase transition of AWV in green wood near the freezing point(as derived from previous experimental results) was plausibly described.By comparative analysis between theoretical and experimental models for American red pine(Pinus resinosa) samples,it was established that the theoretically predicted AWV values matched the experiment results when the temperature of the wood was below the freezing point of water,with an averageprediction error of 1.66%.The theoretically predicted AWV increased quickly in green wood as temperature decreased and changed suddenly near 0 °C,consistent with the experimental observations.The prediction error of the model was relatively large when the temperature of the wood was above the freezing point,probably due to an overestimation of the effect of the liquid water content on the acoustic velocity and the limited variables of the model.The high correlation between the predicted and measured acoustic velocity values in frozen wood samples revealed the mechanisms of temperature,MC,and water status and how these affected the wood(particularly its acoustic velocity below freezing point of water).This result also verified the reliability of a previous experimental model used to adjust for the effect of temperature during field testing of trees.

A Novel Interacting Multiple-Model Method and Its Application to Moisture Content Prediction of ASP Flooding

In this paper,an interacting multiple-model(IMM)method based on datadriven identification model is proposed for the prediction of nonlinear dynamic systems.Firstly,two basic models are selected as combination components due to their proved effectiveness.One is Gaussian process(GP)model,which can provide the predictive variance of the predicted output and only has several optimizing parameters.The other is regularized extreme learning machine(RELM)model,which can improve the overfitting problem resulted by empirical risk minimization principle and enhances the overall generalization performance.Then both of the models are updated continually using meaningful new data selected by data selection methods.Furthermore,recursive methods are employed in the two models to reduce the computational burden caused by continuous renewal.Finally,the two models are combined in IMM algorithm to realize the hybrid prediction,which can avoid the error accumulation in the single-model prediction.In order to verify the performance,the proposed method is applied to the prediction of moisture content of alkali-surfactant-polymer(ASP)flooding.The simulation results show that the proposed model can match the process very well.And IMM algorithm can outperform its components and provide a nice improvement in accuracy and robustness.

Modeling Impact of ROADM Intra-Node Add/Drop Contention on a Single Node Lightpath Blocking Performance

We propose an analytical model to evaluate the lightpath blocking performance for a single ROADM node with intra-node add/drop contention,in which the number of lightpaths that can be added/dropped with the same wavelength is limited by the add/drop contention factor.Different models of traffic load per nodal degree are considered to validate the effectiveness of the analytical model.The simulation results show that the proposed analytical model is effective in predicting the performance for different values of add/drop contention factor C and for variable offered loads at the node.The add/drop contention factor shows an important impact on the lightpath blocking performance and properly raising the contention factor can significantly improve the lightpath blocking performance.When the add/drop contention factor C exceeds a certain level,the performance of a ROADM with intra-node contention is close to that of a contentionless ROADM.

A model of unfrozen water content in rock during freezing and thawing with experimental validation by nuclear magnetic resonance

The unfrozen water content of rock during freezing and thawing has an important influence on its physical and mechanical properties.This study presented a model for calculating the unfrozen water content of rock during freezing and thawing process,considering the influence of unfrozen water film and rock pore structure,which can reflect the hysteresis and super-cooling effects.The pore size distribution cu rves of red sandsto ne and its unfrozen water conte nt under different temperatures during the freezing and thawing process were measured using nuclear magnetic resonance(NMR) to validate the proposed model.Comparison between the experimental and calculated results indicated that the theoretical model accu rately reflected the water content change law of red sandstone during the freezing and thawing process.Furthermore,the influences of Hamaker constant and surface relaxation parameter on the model results were examined.The results showed that the appropriate magnitude order of Hamaker constant for the red sandstone was 10J to 10J;and when the relaxation parameter of the rock surface was within 25-30 μm/ms,the calculated unfrozen water content using the proposed model was consistent with the experimental value.

Physical model investigation on effects of drainage condition and cement addition on consolidation behavior of tailings slurry within backfilled stopes

Estimation of stressses within the tailings slurry during self-weight consolidation is a critical issue for cost-effective barricade design and efficient backfill planning in underground mine stopes.This process requires a good understanding of self-weight consolidation behaviors of the tailings slurry within practical stopes,where many factors can have significant effects on the consolidation,including drainage condition and cement addition.In this paper,the prepared tailings slurry with different cement contents(0,4.76wt%,and 6.25wt%)was poured into1.2 m-high columns,which allowed three drainage scenarios(undrained,partial lateral drainage near the bottom part,and full lateral drainage boundaries)to investigate the effects of drainage condition and cement addition on the consolidation behavior of the tailings slurry.The consolidation behavior was analyzed in terms of pore water pressure(PWP),settlement,volume of drainage water,and residual water content.The results indicate that increasing the length of the drainage boundary or cement content aids in PWP dissipation.In addition,constructing an efficient drainage boundary was more favorable to PWP dissipation than increasing cement addition.The final stable PWP on the column floor was not sensitive to cement addition.The final settlement of uncemented tailings slurry was independent of drainage conditions,and that of cemented tailings slurry decreased with the increase in cement addition.Notably,more pore water can drain out from the cemented tailings slurry than the uncemented tailings slurry during consolidation.

DYNAMIC MODELS OF AQUIFEROUS CONTENT OF LIVING FUELS

Forest fire occurrence is closely relative with fuel water content. There are a lot of research about dead fuels. but forest fuels consist of both dead fuels and living fuels. Each large fire occurrence has a good relationship with living fuels. Especially living fuels can influence the production and development of big forest fire, so, we selected Tahe, in Daxingan Mountains, as observation site. According to actual data,we can establish a set of models of different living fuel water content variation with linear -regression method.

A Digital Right Management Model for Distribution of Digital Audiovisual Content

This paper presents a digital rights management model, which considers the integrated factors including legality, communication security, integrity of the content, and trading fairness. The architecture of the model, the necessary protocol for the copyright control and content distribution, the authentication mechanism which offer consumption registration for content fair distribution, of the model are all provided. The scheme also provides distribution and evidence for using the copyright of digital content fairly and effectively. Finally, analysis shows the proposed model has both high security and good performance.

A comparison of the CMIP5 models on the historical simulation of the upper ocean heat content in the South China Sea

Seventeen models participating in the Coupled Model Intercomparison Project phase 5（CMIP5） activity are compared on their historical simulation of the South China Sea（SCS） ocean heat content（OHC） in the upper 300 m. Ishii＇s temperature data, based on the World Ocean Database 2005（WOD05） and World Ocean Atlas 2005（WOA05）, is used to assess the model performance by comparing the spatial patterns of seasonal OHC anomaly（OHCa） climatology, OHC climatology, monthly OHCa climatology, and interannual variability of OHCa. The spatial patterns in Ishii＇s data set show that the seasonal SCS OHCa climatology, both in winter and summer, is strongly affected by the wind stress and the current circulations in the SCS and its neighboring areas. However, the CMIP5 models present rather different spatial patterns and only a few models properly capture the dominant features in Ishii＇s pattern. Among them, GFDL-ESM2 G is of the best performance. The SCS OHC climatology in the upper 300 m varies greatly in different models. Most of them are much greater than those calculated from Ishii＇s data. However, the monthly OHCa climatology in each of the 17 CMIP5 models yields similar variation and magnitude as that in Ishii＇s. As for the interannual variability, the standard deviations of the OHCa time series in most of the models are somewhat larger than those in Ishii＇s. The correlation between the interannual time series of Ishii＇s OHCa and that from each of the 17 models is not satisfactory. Among them, BCC-CSM1.1 has the highest correlation to Ishii＇s, with a coefficient of about 0.6.

Textual Content Prediction via Fuzzy Attention Neural Network Model without Predefined Knowledge

Text analysis is a popular technique for finding the most significant information from texts including semantic,emotional,and other hidden features,which became a research hotspot in the last few years.Specially,there are some text analysis tasks with judgment reports,such as analyzing the criminal process and predicting prison terms.Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge.All these methods require a lot of time and labor costs.Therefore,in this paper,we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge.We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term.The procedure in our framework includes information extraction,term fuzzification,and document vector regression.We carry out experiments with real-world judgment reports and compare our model’s performance with those of ten traditional classification and regression models and two deep learning models.The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics.Finally,we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.