Modeling and Prediction of Coal Ash Fusion Temperature based on BP Neural Network
Coal ash is the residual generated from combustion of coal. The ash fusion temperature (AFT) of coal gives detail information on the suitability of a coal source for gasification procedures, and specifically to which extent ash agglomeration or clinkering is likely to occur within the gasifier. To investigate the contribution of oxides in coal ash to AFT, data of coal ash chemical compositions and Softening Temperature (ST) in different regions of China were collected in this work and a BP neural network model was established by XD-APC PLATFORM. In the BP model, the inputs were the ash compositions and the output was the ST. In addition, the ash fusion temperature prediction model was obtained by industrial data and the model was generalized by different industrial data. Compared to empirical formulas, the BP neural network obtained better results. By different tests, the best result and the best configurations for the model were obtained: hidden layer nodes of the BP network was setted as three, the component contents (SiO2, Al2O3, Fe2O3, CaO, MgO) were used as inputs and ST was used as output of the model.
Statistical Model for Prediction of Ash Fusion Temperatures from Additive Doped Biomass
The prediction of phase transformation of biomass ashes is challenging due to the highly variable composition of these fuels as well as the complex processes accompanying phase transformations. The AFT (Ash Fusion Temperature) model was performed in Statistica 13.1 software. This model was divided into three separate submodels, which were designed to predict the characteristic ash melting temperatures for raw and modified biomass. It is based on the chemical composition of fuel and ash as obtained using ash analysis standards. For the discussed models, several coefficients describing multiple regression parameters are presented. The AFT model discussed in this article is suitable for predicting ash fusion temperatures for biomass and allows for the prediction of the temperature with an average error of <±70.05 °C for IDT; <±51.98 °C for HT; <±47.52 °C for FT for raw biomass. For some of the additionally tested biomass, a value higher than the average difference between the measured temperature and the designated model was observed (<90 °C). Moreover, morphological analyses of the structure SEM-EDS for ash samples with and without additive were performed.
Therapist-case managers: more than brokers of services.
For satisfactory adjustment into the community, the long-term patient often needs assistance in dealing with a bureaucracy of agencies and departments. Some professionals have suggested establishing the role of case manager, but the case manager system is susceptible to becoming an impersonal bureaucracy itself. The author suggests that case management functions are part of the normal duties of a conscientious therapist, and that only through significant therapeutic involvement does a case manager acquire the in-depth knowledge of the patient to adequately assess his needs and facilitate the processes for meeting them. Thus the case manager should be not simply an intermediate broker of services but the patient's primary therapist. With the "therapist-case manager," the functions of therapy and case management would be combined. If therapists fail to do case management or to treat long-term patients altogether, this problem should be dealt with directly rather than simply by adding another member to the team.