Prediction Model of Refined Gasoline Blending Formula Based on PSO-DBN

To address the problem of the low accuracy of refined gasoline blending formula in the petrochemical industry,the advantages of deep belief networks(DBNs)in feature extraction and nonlinear processing are considered,and they are applied to the prediction modeling of refined gasoline blending conservative formula.Firstly,based on historical measured data of refined gasoline blending and according to the characteristics of the data set,we use bootstrapping to divide the training data set and the test data set.Secondly,considering that parameter selection for the network is difficult,particle swarm optimization is adopted to improve the related optimal parameters and replace the tedious process of manually selecting parameters,greatly improving optimization efficiency.In addition,the contrastive divergence algorithm is used for unsupervised forward feature learning and supervised reverse fine-tuning of the network,so as to construct a more accurate prediction model for conservative formula.Finally,in order to evaluate the effectiveness of this method,the simulation results are compared with those of traditional modeling methods,which show that the DBNs has better prediction performance than error back propagation and support vector machines,and can provide production guidance for refined gasoline blending formula.

Thoughts on Stimulating the Employee’s Work Efficiency in State-Owned Enterprises

State-owned enterprises are the basic guarantee of China’s economic and social development,the leading force in the field of strategic competition,but also the main provider of national social wealth accumulation.Although in many strategic areas,the operating efficiency of state-owned enterprises cannot be simply measured by the input profit margin;state-owned enterprises use state and social resources to operate.Shouldering the responsibility of preserving and increasing the value of state-owned assets also is the responsibility of all state-owned enterprise managers and employees.However,the management efficiency of state-owned enterprises is relatively low,and employees’performance management and incentives have been lack of innovation and improvement.This paper analyzes the current state-owned enterprises in China,the characteristics of performance management,as well as the problems faced,and discusses the measures to encourage the efficiency of state-owned enterprises employees.

Thermodynamic,Economic,and Environmental Analyses and Multi-Objective Optimization of Dual-Pressure Organic Rankine Cycle System with Dual-Stage Ejector

A novel dual-pressure organic Rankine cycle system(DPORC)with a dual-stage ejector(DE-DPORC)is proposed.The system incorporates a dual-stage ejector that utilizes a small amount of extraction steam from the highpressure expander to pressurize a large quantity of exhaust gas to performwork for the low-pressure expander.This innovative approach addresses condensing pressure limitations,reduces power consumption during pressurization,minimizes heat loss,and enhances the utilization efficiency of waste heat steam.A thermodynamic model is developed with net output work,thermal efficiency,and exergy efficiency(W_(net,ηt,ηex))as evaluation criteria,an economicmodel is established with levelized energy cost(LEC)as evaluation index,anenvironmentalmodel is created with annual equivalent carbon dioxide emission reduction(AER)as evaluation parameter.A comprehensive analysis is conducted on the impact of heat source temperature(T_(S,in)),evaporation temperature(T_(2)),entrainment ratio(E_(r1),E_(r2)),and working fluid pressure(P_(5),P_(6))on system performance.It compares the comprehensive performance of the DE-DPORC system with that of the DPORC system at TS,in of 433.15 K and T2 of 378.15 K.Furthermore,multi-objective optimization using the dragonfly algorithm is performed to determine optimal working conditions for the DE-DPORC system through the TOPSIS method.The findings indicate that the DEDPORC system exhibits a 5.34%increase inWnet andηex,a 58.06%increase inηt,a 5.61%increase in AER,and a reduction of 47.67%and 13.51%in the heat dissipation of the condenser andLEC,compared to theDPORCsystem,highlighting the advantages of this enhanced system.The optimal operating conditions are TS,in=426.74 K,T_(2)=389.37 K,E_(r1)=1.33,E_(r2)=3.17,P_(5)=0.39 MPa,P_(6)=1.32 MPa,which offer valuable technical support for engineering applications;however,they are approaching the peak thermodynamic and environmental performance while falling short of the highest economic performance.

Zeolite-encaged Ultrasmall Pt-Zn Species with Trace Amount of Pt for Efficient Propane Dehydrogenation

Propane dehydrogenation(PDH)has become a globe-welcoming technology to meet the massive demand for propylene,but the most commonly used Pt-based catalysts suffer from quick sintering,poor selectivity for propylene,and unsatisfied Pt utilization.Herein,a series of Silicalite-1(S-1)zeolite-encaged ultrasmall Pt-Zn clusters with a trace amount of Pt[40—180 ppm(parts per million)]were developed by using a one-pot ligand-protected direct H_(2) reduction method.Interestingly,the extremely low amount of Pt can significantly promote the activity of zeolite-encaged Zn catalysts in PDH reactions.Thanks to the high Pt dispersion,the synergy between Pt and Zn species,and the confinement effect of zeolites,the optimized PtZn@S-1 catalyst with 180 ppm Pt and 1.88%(mass fraction)Zn,exhibited an extraordinarily high propane conversion(33.9%)and propylene selectivity(99.5%)at 550℃with a weight hourly space velocity(WHSV)of 8 h^(-1),affording an extremely high propylene formation rate of 340.7 mol_(C_(3)H_(6))·g_(Pt^(-1))·h^(-1).This work provides a reference for the preparation of zeolite-encaged metal catalysts with high activity and noble metal utilization in PDH reactions.