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We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Simplicity, efficiency and precision are basic principles for modeling and analyzing of coal-fired boilers data. However, the load fluctuations, system delay, multi-variable coupling pose great challenges to the high-precision modeling for NOx emission. A hybrid feature selection method for boiler data is proposed to predict accurately NOx emission. Firstly, the mechanism analysis is used to narrow the feature scope, and the feature set is selected preliminarily. Secondly, maximum information coefficient (MIC) method is introduced to calculate the correlation between features and NOx information to eliminate boiler system delay. Thirdly, a combined feature evaluation method is developed, which integrates Filter and Embedded method to obtain feature ranking, then the ranking information is regarded as priori knowledge to improve the genetic algorithm. Finally, a fitness function to maximize prediction accuracy and minimize feature dimension is constructed based on hybrid method to realize feature subset selection and NOx emission prediction. Original data are collected from one 1000 MW coal-fired unit in the Guangdong province of China. Experimental results show that the number of features is reduced by nearly 70%, the MAPE of LightGBM regression model is no more than 2%. Higher prediction accuracy can be obtained using the features extracted through the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Air pollution is a serious problem in China that urgently needs to be addressed. Air pollution has a great impact on the lives of citizens and on urban development. The particulate matter (PM) value is usually used to indicate the degree of air pollution. In addition to that of PM 2.5 and PM 10 , the use of the PM 2.5 / PM 10 ratio as an indicator and assessor of air pollution has also become more widespread. This ratio reflects the air pollution conditions and pollution sources. In this paper, a better composite prediction system aimed at improving the accuracy and spatiotemporal applicability of PM 2.5 / PM 10 was proposed. First, the aerosol optical depth (AOD) in 2017 in Wuhan was obtained based on Moderate Resolution Imaging Spectroradiometer (MODIS) images, with a 1 km spatial resolution, by using the dense dark vegetation (DDV) method. Second, the AOD was corrected by calculating the planetary boundary layer height (PBLH) and relative humidity (RH). Third, the coefficient of determination of the optimal subset selection was used to select the factor with the highest correlation with PM 2.5 / PM 10 from meteorological factors and gaseous pollutants. Then, PM 2.5 / PM 10 predictions based on time, space, and random patterns were obtained by using nine factors (the corrected AOD, meteorological data, and gaseous pollutant data) with the long short-term memory (LSTM) neural network method, which is a dynamic model that remembers historical information and applies it to the current output. Finally, the LSTM model prediction results were compared and analyzed with the results of other intelligent models. The results showed that the LSTM model had significant advantages in the average, maximum, and minimum accuracy and the stability of PM 2.5 / PM 10 prediction. [ABSTRACT FROM AUTHOR]

Air pollution is a serious and urgent problem in China, and it has a great impact on the lives of residents and urban development. The particulate matter (PM) value is usually used to indicate the degree of air pollution. In addition to PM2.5 and PM10, the use of the PM2.5 / PM10 ratio as an indicator and assessor of air pollution has also become more widespread. This ratio reflects the air pollution conditions and pollution sources. In this paper, a better composite prediction system was proposed that aimed at improving the accuracy and spatio-temporal applicability of PM2.5 / PM10. First, the aerosol optical depth (AOD) in 2017 in Wuhan was obtained based on Moderate Resolution Imaging Spectroradiometer images, with a 1 km spatial resolution, by using the Dense Dark Vegetation method. Second, the AOD was corrected by calculating the planetary boundary layer height and relative humidity. Third, the coefficient of determination of the optimal subset selection was used to select the factor with the highest correlation with PM2.5 / PM10 from meteorological factors and gaseous pollutants. Then, PM2.5 / PM10 predictions based on time, space, and random patterns were obtained by using 9 factors (the corrected AOD, meteorological data and gaseous pollutant data) with the long short-term memory (LSTM) neural network method, which is a dynamic model that remembers historical information and applies it to the current output. Finally, the LSTM model prediction results were compared and analysed with the results of other intelligent models. The results showed that the LSTM model had significant advantages in the average, maximum and minimum accuracies and the stability of PM2.5 / PM10 prediction. [ABSTRACT FROM AUTHOR]

• We propose a SEE_DM model to be extended and the local conditions in northern China to be adopted by the algorithms implemented in the model components, it is able to warn the onset date and estimate incidence trends of downy mildew on grapes. • It was proved that SEE_DM is accuracy in reproducing observed data after an extensive evaluation with multiyear and multisite experimental trial. • Incidence trends under weather variability confirm northern China is gradually becoming the area with heavy grape downy mildew incidence. Grape downy mildew is a major biotic constraint to grapevine production worldwide, and its impact is influenced by environmental conditions and varietal susceptibility. Here, we proposed a grape downy mildew model to optimize plant disease management and its application on northern Chinese grapevine areas. A primary and a secondary infection model of P. viticola were integrated to estimate the date of symptoms on set and disease incidence, to give the firSt brEakout and incidEnce of grape Downy Mildew model (SEE_DM). The experimental data for model calibration were collected on two grapevine varietals with high and moderate susceptibility to downy mildew grown in a multiyear (2009, 2011–2019) and multisite (Beijing, Shenyang and Yantai) trial. A model sensitivity analysis (Sobol analysis) drove the selection of the subset of relevant parameters to be adjusted in calibration. The model adequately reproduced the observed downy mildew incidence, obtaining high R2 (0.89), Nash-Sutcliffe modelling efficiency (0.72), and low RMSE (9–16%). The model correctly estimated the disease onset date in all conditions but two (late by 5 and 13 days), demonstrating to be a valid tool to take timely decision to limit the time course of the disease. The model was then projected in three sites under varying temperature and rainfall to analyses their effects on the trends of grape downy mildew incidence, using a variety with moderate susceptibility. This study highlights that Northern China is gradually becoming more suitable for grape downy mildew infections, along with warmer temperatures and more frequent rainfall. [ABSTRACT FROM AUTHOR]

A data-driven model framework integrating Feature Subset Selection (FSS), production pattern clustering analysis and prediction was proposed for predicting ethylene yield of ethylene plant by using the massive sensing data recorded by the Distributed Control System (DCS) of petrochemical enterprises. Firstly, an Ensemble-Filter FSS model based on three different metrics is designed to initially filter all the steam cracking furnace features, and then a Wrapper FSS model based on GA-SVR is used to obtain the optimal subset of features affecting ethylene yield. The steam cracking furnace was identified based on the Density Peak Clustering (DPC) algorithm based on the production patterns embedded in the data. Ethylene yield prediction models were separately developed for each production pattern to summarize the final prediction results. The proposed model was validated against historical data from an industrial steam cracking furnace in northwest China. Results show that the number of features have a 93.4% reduction in the FSS stage. and a 40.6% reduction in predicted MSE. Compared with the benchmark ANN model, the proposed DPNN model has a 56.6% reduction in MSE based on the optimal cluster result. What's more, the proposed framework has a strong generalization ability and with a modular structure which is easy to modify., which is expected to be used to guide the ethylene plant operating in reasonable intervals. • A novel data-driven model framework of steam cracking process is proposed. • The dataset used in this work has a large size coming from an industrial plant. • Feature number is reduced by 93.4% in feature subset selection stage. • Optimal feature subset is highly correlated with the process experience. • Modeling with production patterns identified data can improve the model performance. [ABSTRACT FROM AUTHOR]