Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Nomogram to predict severe retinopathy of prematurity in Southeast China

AIM:To define the predictive factors of severe retinopathy of prematurity(ROP)and develop a nomogram for predicting severe ROP in southeast China.METHODS:Totally 554 infants diagnosed with ROP hospitalized in the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University and hospitalized in Taizhou Women and Children’s Hospital were included.Clinical data and 43 candidate predictive factors of ROP infants were collected retrospectively.Logistic regression model was used to identify predictive factors of severe ROP and to propose a nomogram for individual risk prediction,which was compared with WINROP model and Digirop-Birth model.RESULTS:Infants from the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University(n=478)were randomly allocated into training(n=402)and internal validation group(n=76).Infants from Taizhou Women and Children’s Hospital were set as external validation group(n=76).Severe ROP were found in 52 of 402 infants,12 of 76 infants,and 7 of 76 infants in training group,internal validation group,and external validation group,respectively.Birth weight[odds ratio(OR),0.997;95%confidence interval(CI),0.996-0.999;P<0.001],multiple births(OR,1.885;95%CI,1.013-3.506;P=0.045),and non-invasive ventilation(OR,0.288;95%CI,0.146-0.570;P<0.001)were identified as predictive factors for the prediction of severe ROP,by univariate analysis and multivariate analysis.For predicting severe ROP based on the internal validation group,the areas under receiver operating characteristic curve(AUC)was 78.1(95%CI,64.2-92.0)for the nomogram,32.9(95%CI,15.3-50.5)for WINROP model,70.2(95%CI,55.8-84.6)for Digirop-Birth model.In external validation group,AUC of the nomogram was also higher than that of WINROP model and Digirop-Birth model(80.2 versus 51.1 and 63.4).The decision curve analysis of the nomogram demonstrated better clinical efficacy than that of WINROP model and Digirop-Birth model.The calibration curves demonstrated a good consistency between the actual severe ROP incidence and the predicted probability.CONCLUSION:Birth weight,multiple births,and noninvasive ventilation are independent predictors of severe ROP.The nomogram has a good ability to predict severe ROP and performed well on internal validation and external validation in southeast China.

Assimilating FY-4A AGRI Radiances with a Channel-Sensitive Cloud Detection Scheme for the Analysis and Forecasting of Multiple Typhoons

This paper presents an attempt at assimilating clear-sky FY-4A Advanced Geosynchronous Radiation Imager(AGRI)radiances from two water vapor channels for the prediction of three landfalling typhoon events over the West Pacific Ocean using the 3DVar data assimilation(DA)method along with the WRF model.A channel-sensitive cloud detection scheme based on the particle filter(PF)algorithm is developed and examined against a cloud detection scheme using the multivariate and minimum residual(MMR)algorithm and another traditional cloud mask–dependent cloud detection scheme.Results show that both channel-sensitive cloud detection schemes are effective,while the PF scheme is able to reserve more pixels than the MMR scheme for the same channel.In general,the added value of AGRI radiances is confirmed when comparing with the control experiment without AGRI radiances.Moreover,it is found that the analysis fields of the PF experiment are mostly improved in terms of better depicting the typhoon,including the temperature,moisture,and dynamical conditions.The typhoon track forecast skill is improved with AGRI radiance DA,which could be explained by better simulating the upper trough.The impact of assimilating AGRI radiances on typhoon intensity forecasts is small.On the other hand,improved rainfall forecasts from AGRI DA experiments are found along with reduced errors for both the thermodynamic and moisture fields,albeit the improvements are limited.

Heterogeneous information phase space reconstruction and stability prediction of filling body–surrounding rock combination

Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.

Regression analysis and its application to oil and gas exploration:A case study of hydrocarbon loss recovery and porosity prediction,China

In oil and gas exploration,elucidating the complex interdependencies among geological variables is paramount.Our study introduces the application of sophisticated regression analysis method at the forefront,aiming not just at predicting geophysical logging curve values but also innovatively mitigate hydrocarbon depletion observed in geochemical logging.Through a rigorous assessment,we explore the efficacy of eight regression models,bifurcated into linear and nonlinear groups,to accommodate the multifaceted nature of geological datasets.Our linear model suite encompasses the Standard Equation,Ridge Regression,Least Absolute Shrinkage and Selection Operator,and Elastic Net,each presenting distinct advantages.The Standard Equation serves as a foundational benchmark,whereas Ridge Regression implements penalty terms to counteract overfitting,thus bolstering model robustness in the presence of multicollinearity.The Least Absolute Shrinkage and Selection Operator for variable selection functions to streamline models,enhancing their interpretability,while Elastic Net amalgamates the merits of Ridge Regression and Least Absolute Shrinkage and Selection Operator,offering a harmonized solution to model complexity and comprehensibility.On the nonlinear front,Gradient Descent,Kernel Ridge Regression,Support Vector Regression,and Piecewise Function-Fitting methods introduce innovative approaches.Gradient Descent assures computational efficiency in optimizing solutions,Kernel Ridge Regression leverages the kernel trick to navigate nonlinear patterns,and Support Vector Regression is proficient in forecasting extremities,pivotal for exploration risk assessment.The Piecewise Function-Fitting approach,tailored for geological data,facilitates adaptable modeling of variable interrelations,accommodating abrupt data trend shifts.Our analysis identifies Ridge Regression,particularly when augmented by Piecewise Function-Fitting,as superior in recouping hydrocarbon losses,and underscoring its utility in resource quantification refinement.Meanwhile,Kernel Ridge Regression emerges as a noteworthy strategy in ameliorating porosity-logging curve prediction for well A,evidencing its aptness for intricate geological structures.This research attests to the scientific ascendancy and broad-spectrum relevance of these regression techniques over conventional methods while heralding new horizons for their deployment in the oil and gas sector.The insights garnered from these advanced modeling strategies are set to transform geological and engineering practices in hydrocarbon prediction,evaluation,and recovery.

Multi-Time Scale Operation and Simulation Strategy of the Park Based on Model Predictive Control

Due to the impact of source-load prediction power errors and uncertainties,the actual operation of the park will have a wide range of fluctuations compared with the expected state,resulting in its inability to achieve the expected economy.This paper constructs an operating simulation model of the park power grid operation considering demand response and proposes a multi-time scale operating simulation method that combines day-ahead optimization and model predictive control(MPC).In the day-ahead stage,an operating simulation plan that comprehensively considers the user’s side comfort and operating costs is proposed with a long-term time scale of 15 min.In order to cope with power fluctuations of photovoltaic,wind turbine and conventional load,MPC is used to track and roll correct the day-ahead operating simulation plan in the intra-day stage to meet the actual operating operation status of the park.Finally,the validity and economy of the operating simulation strategy are verified through the analysis of arithmetic examples.

Country-based modelling of COVID-19 case fatality rate:A multiple regression analysis

BACKGROUND The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection.The determinants of mortality on a global scale cannot be fully understood due to lack of information.AIM To identify key factors that may explain the variability in case lethality across countries.METHODS We identified 21 Potential risk factors for coronavirus disease 2019(COVID-19)case fatality rate for all the countries with available data.We examined univariate relationships of each variable with case fatality rate(CFR),and all independent variables to identify candidate variables for our final multiple model.Multiple regression analysis technique was used to assess the strength of relationship.RESULTS The mean of COVID-19 mortality was 1.52±1.72%.There was a statistically significant inverse correlation between health expenditure,and number of computed tomography scanners per 1 million with CFR,and significant direct correlation was found between literacy,and air pollution with CFR.This final model can predict approximately 97%of the changes in CFR.CONCLUSION The current study recommends some new predictors explaining affect mortality rate.Thus,it could help decision-makers develop health policies to fight COVID-19.

A new method for predicting injection multiples of extreme displacement in waterflood reservoirs

The theoretical relationship between water injection multiple(i.e.injected pore volume)and water saturation is inferred from theoretical concepts of reservoir engineering.A mathematical model based on core displacement tests is established for the entire injection process that satisfies both initial displacement and extreme displacement,simultaneously.The results show that prior to the flooding,the water injection multiple has a linear relationship with the water saturation,and the utilization rate of the injected water is the highest.As water breakthrough at the production end,the water-cut increases,and the injection multiple increases exponentially while the utilization efficiency of the injected water gradually decreases.When the injection multiple approaches infinity,the utilization efficiency of the injected water gradually decreases to 0,by which time the water-cut at the production end is always 1.At this time,the water saturation no longer changes,and the water flooding recovery rate reaches its limit.Based on the experimental test data,a mathematical model of the entire process of injection multiple and water saturation is established,which has high fitting accuracy that can predict the injection multiple in the different stages of development of a mature oil reservoir.The dynamically changing index of the injection water utilization efficiency in reservoir development by reactive water flooding can be obtained through reasonable transformation of the mathematical model.This is of great significance in guiding evaluations of the effects of reservoir development and formulating countermeasures.

Endpoint Prediction of EAF Based on Multiple Support Vector Machines

The endpoint parameters are very important to the process of EAF steel-making, but their on-line measurement is difficult. The soft sensor technology is widely used for the prediction of endpoint parameters. Based on the analysis of the smelting process of EAF and the advantages of support vector machines, a soft sensor model for predicting the endpoint parameters was built using multiple support vector machines （MSVM）. In this model, the input space was divided by subtractive clustering and a sub-model based on LS-SVM was built in each sub-space. To decrease the correlation among the sub-models and to improve the accuracy and robustness of the model, the sub- models were combined by Principal Components Regression. The accuracy of the soft sensor model is perfectly improved. The simulation result demonstrates the practicability and efficiency of the MSVM model for the endpoint prediction of EAF.

Predicting the growth performance of growing-finishing pigs based on net energy and digestible lysine intake using multiple regression and artificial neural networks models

Backgrounds:Evaluating the growth performance of pigs in real-time is laborious and expensive,thus mathematical models based on easily accessible variables are developed.Multiple regression(MR)is the most widely used tool to build prediction models in swine nutrition,while the artificial neural networks(ANN)model is reported to be more accurate than MR model in prediction performance.Therefore,the potential of ANN models in predicting the growth performance of pigs was evaluated and compared with MR models in this study.Results:Body weight(BW),net energy(NE)intake,standardized ileal digestible lysine(SID Lys)intake,and their quadratic terms were selected as input variables to predict ADG and F/G among 10 candidate variables.In the training phase,MR models showed high accuracy in both ADG and F/G prediction(R^(2)_(ADG)=0.929,R^(2)_(F/G)=0.886)while ANN models with 4,6 neurons and radial basis activation function yielded the best performance in ADG and F/G prediction(R^(2)_(ADG)=0.964,R^(2)_(F/G)=0.932).In the testing phase,these ANN models showed better accuracy in ADG prediction(CCC:0.976 vs.0.861,R^(2):0.951 vs.0.584),and F/G prediction(CCC:0.952 vs.0.900,R^(2):0.905 vs.0.821)compared with the MR models.Meanwhile,the“over-fitting”occurred in MR models but not in ANN models.On validation data from the animal trial,ANN models exhibited superiority over MR models in both ADG and F/G prediction(P<0.01).Moreover,the growth stages have a significant effect on the prediction accuracy of the models.Conclusion:Body weight,NE intake and SID Lys intake can be used as input variables to predict the growth performance of growing-finishing pigs,with trained ANN models are more flexible and accurate than MR models.Therefore,it is promising to use ANN models in related swine nutrition studies in the future.

Interacting mechanism and initiation prediction of multiple cracks

The maximum Mode Ⅰ and Mode Ⅱ stress intensity factors(SIFs), KI,kmax(θ) and KII,kmax(θ)(0°<θ<360°), of inclined parallel multi-crack varying with relative positions(including horizontal and vertical spacings) are calculated by the complex function and integration method to analyze their interacting mechanism and determine the strengthening and weakening zone of SIFs. The multi-crack initiation criterion is established based on the ratio of maximum tension-shear SIF to predict crack initiation angle, load, and mechanism. The results show that multi-crack always initiates in Mode Ⅰ and the vertical spacing is better not to be times of half crack-length for crack-arrest, which is in good agreement with test results of the red-sandstone cube specimens with three parallel cracks under uniaxial compression. This can prove the validity of the multi-crack initiation criterion.

A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction

This paper highlights the statistical procedure used in developing models that have the ability of capturing and forecasting the traffic of mobile communication network operating in Vietnam. To build such models, we follow Box-Jenkins method to construct a multiplicative seasonal ARIMA model to represent the mean component using the past values of traffic, then incorporate a GARCH model to represent its volatility. The traffic is collected from EVN Telecom mobile communication network. Diagnostic tests and examination of forecast accuracy measures indicate that the multiplicative seasonal ARIMA/GARCH model, i.e. ARIMA (1, 0, 1) × (0, 1, 1)24/GARCH (1, 1) shows a good estimation when dealing with volatility clustering in the data series. This model can be considered to be a flexible model to capture well the characteristics of EVN traffic series and give reasonable forecasting results. Moreover, in such situations that the volatility is not necessary to be taken into account, i.e. short-term prediction, the multiplicative seasonal ARIMA/GARCH model still acts well with the GARCH parameters adjusted to GARCH (0, 0).

Multiple sclerosis：integration of modeling with biology,clinical and imaging measures to provide better monitoring of disease progression and prediction of outcome

Multiple Sclerosis（MS） is a major cause of neurological disability in adults and has an annual cost of approximately $28 billion in the United States. MS is a very complex disorder as demyelination can happen in a variety of locations throughout the brain; therefore, this disease is never the same in two patients making it very hard to predict disease progression. A modeling approach which combines clinical, biological and imaging measures to help treat and fight this disorder is needed. In this paper, I will outline MS as a very heterogeneous disorder, review some potential solutions from the literature, demonstrate the need for a biomarker and will discuss how computational modeling combined with biological, clinical and imaging data can help link disparate observations and decipher complex mechanisms whose solutions are not amenable to simple reductionism.

Sound Quality Prediction of Vehicle Interior Noise under Multiple Working Conditions Using Back-Propagation Neural Network Model

This paper presents a back-propagation neural network model for sound quality prediction (BPNN-SQP) of multiple working conditions’ vehicle interior noise. According to the standards and regulations, four kinds of vehicle interior noises under operating conditions, including idle, constant speed, accelerating and braking, are acquired. The objective psychoacoustic parameters and subjective annoyance results are respectively used as the input and output of the BPNN-SQP model. With correlation analysis and significance test, some psychoacoustic parameters, such as loudness, A-weighted sound pressure level, roughness, articulation index and sharpness, are selected for modeling. The annoyance values of unknown noise samples estimated by the BPNN-SQP model are highly correlated with the subjective annoyances. Conclusion can be drawn that the proposed BPNN-SQP model has good generalization ability and can be applied in sound quality prediction of vehicle interior noise under multiple working conditions.

Performance Prediction Based on Statistics of Sparse Matrix-Vector Multiplication on GPUs

As one of the most essential and important operations in linear algebra, the performance prediction of sparse matrix-vector multiplication (SpMV) on GPUs has got more and more attention in recent years. In 2012, Guo and Wang put forward a new idea to predict the performance of SpMV on GPUs. However, they didn’t consider the matrix structure completely, so the execution time predicted by their model tends to be inaccurate for general sparse matrix. To address this problem, we proposed two new similar models, which take into account the structure of the matrices and make the performance prediction model more accurate. In addition, we predict the execution time of SpMV for CSR-V, CSR-S, ELL and JAD sparse matrix storage formats by the new models on the CUDA platform. Our experimental results show that the accuracy of prediction by our models is 1.69 times better than Guo and Wang’s model on average for most general matrices.

Inclusive Multiple Models(IMM)for predicting groundwater levels and treating heterogeneity

An explicit model management framework is introduced for predictive Groundwater Levels(GWL),particularly suitable to Observation Wells(OWs)with sparse and possibly heterogeneous data.The framework implements Multiple Models(MM)under the architecture of organising them at levels,as follows:(i)Level 0:treat heterogeneity in the data,e.g.Self-Organised Mapping(SOM)to classify the OWs;and decide on model structure,e.g.formulate a grey box model to predict GWLs.(ii)Level 1:construct MMs,e.g.two Fuzzy Logic(FL)and one Neurofuzzy(NF)models.(iii)Level 2:formulate strategies to combine the MM at Level 1,for which the paper uses Artificial Neural Networks(Strategy 1)and simple averaging(Strategy 2).Whilst the above model management strategy is novel,a critical view is presented,according to which modelling practices are:Inclusive Multiple Modelling(IMM)practices contrasted with existing practices,branded by the paper as Exclusionary Multiple Modelling(EMM).Scientific thinking over IMMs is captured as a framework with four dimensions:Model Reuse(MR),Hierarchical Recursion(HR),Elastic Learning Environment(ELE)and Goal Orientation(GO)and these together make the acronym of RHEO.Therefore,IMM-RHEO is piloted in the aquifer of Tabriz Plain with sparse and possibly heterogeneous data.The results provide some evidence that(i)IMM at two levels improves on the accuracy of individual models;and(ii)model combinations in IMM practices bring‘model-learning’into fashion for learning with the goal to explain baseline conditions and impacts of subsequent management changes.

Nonlinear Multiple Model Predictive Control of Solution Polymerization of Methyl Methacrylate

A sequential linearized model based predictive controller is designed using the DMC algorithm to control the temperature of a batch MMA polymerization process. Using the mechanistic model of the polymerization, a parametric transfer function is derived to relate the reactor temperature to the power of the heaters. Then, a multiple model predictive control approach is taken in to track a desired temperature trajectory.The coefficients of the multiple transfer functions are calculated along the selected temperature trajectory by sequential linearization and the model is validated experimentally. The controller performance is studied on a small scale batch reactor.

Rheological Behaviour for Polymer Melts and Concentrated Solutions Part Ⅰ:A New Multiple Reptation Model to Predict the Nonlinear Visco-elasticity with Nagai Chain Constraints in Entangled Polymer Melts

An approach of stochastically statistical mechanics and a unified molecular theory of nonlinear viscoelasticity with constraints of Nagai chain entanglement for polymer melts have been proposed. A multimode model structure for a single polymer chain with n tail segments and N reversible entanglement sites on the test polymer chain is developed. Based on the above model structure and the mechanism of molecular flow by the dynamical reorganization of entanglement sites, the probability distribution function of the end-to-end vectr for a single polymer chain at entangled state and the viscoelastic free energy of deformation for polymer melts are calculated by using the method of the stochastically statistical mechanics. The four types of stress-strain relation and the memory function are derived from this thery. The above theoretical relations are verified by the experimentaf data for various polymer melts. These relations are found to be in good agreement with the experimental results

Internal Multiple Prediction Based on Imaging Profile Prediction and Kirchhoff Demigration

This paper introduces an internal multiple prediction method based on imaging profile prediction and Kirchhoff demigration.First,based on an inputted prestack time migration profile,the method predicts the prestack time migration profile that only includes internal multiples by inverse scattering series method.Second,the method uses velocity-weighted Kirchhoff demigration to create shot gathers that contains only internal multiples.Internal multiple prediction based on the prestack time migration profile effectively reduces the computational cost of multiple predictions,and the internal-multiple shot gathers created by Kirchhoff demigration remarkably reduces the complexity of the practical problem.Internal multiple elimination can be conducted through the combined adaptive multiple subtraction based on event tracing.Synthetic and field data tests show that the method effectively predicts internal multiples and possesses considerable potential in field data processing,particularly in areas where internal multiples develop seriously.