Results using simulated process measurements

Using the concentration and obscuration measurements allow all of the kinetic parameters of interest to be identified. The simulated data is shown in Figures 1-2. The parameter estimation results corresponding to these measurements are given in Table 1. These results indicate that these measurements may provide enough process information to allow identification, even in the presence of measurement noise. This hypothesis is investigated experimentally in the next section. 

The mathematical model gives the input and output vectors for the ANN, which, in normal cases, are represented by the measured data. When the learning process has been completed, the process mathematical model (PMM) and the optimizing algorithm (OA) are decoupled and the ANN is ready to produce the simulation results for the process. This procedure is also used to produce the ANN simulators needed for the control of the processes or their usual automatic operation. 

As with previous assessments (e.g. Carter, 2000), the quantity D(Oj-NO) was used as the main criterion of model performance. This quantity is defined as D(03-NO)t = [Oajt -[NO]t - ([Osjo - PMO]o), where [03]o [NO]o, and [Osh, [NO]t are the concentrations of O3 and NO at the beginning of the run, and at time t , respectively. As described in detail previously (e.g., Carter and Lurmann, 1991, Carter et al, 1995a, Carter, 2000), D(Os-NO) is an indicator of the ability of the mechanism to simulate the chemical processes fliat cause O3 formation, giving a useful measure, even when O3 is suppressed by the presence of excess NO. In addition, use of this measure allows a direct comparison wifli die SAPRC-99 published results. The precursor decay rate and formation of die carbonyl products and PAN were also used as criteria of model praformance. 

The protein powder model discussed for the low T crossover, was thus used to analyze measured QENS spectra of the protein hydration water for temperatures ranging from 290 to 380K, covering the first stage of the denaturation process, occurring at the reversible protein denaturation temperature around 34 5K. In Joint was also developed a MD simulation study for the same process, the main obtained results are exposed in the following. Details are the following lysozyme... 

In Table 1, the simulated results are compared with the ones obtained by the original model and in the industrial plant, using the same chip flow rate, liquor concentration and flow rate, feed temperature and equipment geometry. The simulated results are in accordance with the available measured data from the industrial unit. The difference in the pulp yield is probably due to the unavailability of wood composition data. The wood component concentrations were estimated based on the results of analysis in the industrial plant and values of the literature. Another reason could be the dispersion coefficient, which inference may have led to higher effective alkali consumption in the simulated process, increasing the degradation of the hemicelluloses. 

Monte Carlo simulation is used when the model responses are described stochastically, i.e. some of them are not deterministic, rather they are described by random distributions. In this situation the simulation has to be run repeatedly to obtain several independent realizations of the same process. These are used to build the probabilistic distribution of the results, especially the average measures. 

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