Specific Energy Consumption Analysis Model and Its Application in Typical Steel Manufacturing Process

Theoretical minimum and actual specific energy consumptions （SEC） of typical manufacturing process （SMP） were studied. Firstly, a process division of a typical SMP in question was conducted with the theory of SEC analysis. Secondly, an exergy analysis model of a subsystem consisting of several parallel processes and a SEC analysis model of SMP were developed. And finally, based on the analysis models, the SEC of SMP was analyzed by means of the statistical significance. The results show that the SEC of typical SMP comprises the theoretical minimum SEC and the additional SEC derived from the irreversibility~ and the SMP has a theoretical minimum SEC of 6.74 GJ/t and an additional SEC of 19.32 GJ/t, which account for 25.88% and 74.12% of the actual SEC, respectively.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

OPTIMAL INVESTMENT CONSUMPTION MODEL WITH A HIGHER INTEREST RATE FOR BORROWING

This paper considers a consumption and investment decision problem with a higher interest rate for borrowing as well as the dividend rate.Wealth is divided into a riskless asset and risky asset with logrithmic Brownian motion price fluctuations.The stochastic control problem of maximizating expected utility from terminal wealth and consumption is studied.Equivalent conditions for optimality are obtained.By using duality methods,the existence of optimal portfolio consumption is proved,and the explicit solutions leading to feedback formulae are derived for deteministic coefficients.

Analysis of Copper Processing Products Production and Copper Consumption in China

China is the world’s largest producer of copper processing products as well as the world’s largest copper consumer market.In 2013,China produced more than 13.60 million tons of copper processing products,accounting for60%of the world’s total production.Since then,this proportion has expanded year by year,and will increase to about 66.7%in 2016.Meanwhile,China consumed nearly 11

Could wastewater analysis be a useful tool for China?——A review

Analysing wastewater samples is an innovative approach that overcomes many limitations of traditional surveys to identify and measure a range of chemicals that were consumed by or exposed to people living in a sewer catchment area. First conceptualised in 2001, much progress has been made to make wastewater analysis（WWA） a reliable and robust tool for measuring chemical consumption and/or exposure. At the moment, the most popular application of WWA, sometimes referred as sewage epidemiology, is to monitor the consumption of illicit drugs in communities around the globe, including China. The approach has been largely adopted by law enforcement agencies as a device to monitor the temporal and geographical patterns of drug consumption. In the future, the methodology can be extended to other chemicals including biomarkers of population health（e.g. environmental or oxidative stress biomarkers, lifestyle indicators or medications that are taken by different demographic groups） and pollutants that people are exposed to（e.g. polycyclic aromatic hydrocarbons,perfluorinated chemicals, and toxic pesticides）. The extension of WWA to a huge range of chemicals may give rise to a field called sewage chemical-information mining（SCIM） with unexplored potentials. China has many densely populated cities with thousands of sewage treatment plants which are favourable for applying WWA/SCIM in order to help relevant authorities gather information about illicit drug consumption and population health status. However, there are some prerequisites and uncertainties of the methodology that should be addressed for SCIM to reach its full potential in China.