Summary: A “series” hybrid model based on material balances and artificial neural networks to predict the evolution of weight average molecular weight, , in semicontinuous emulsion polymerization with long chain branching kinetics is presented. The core of the model is composed by two artificial neural networks (ANNs) that calculate polymerization rate, Rp, and instantaneous weight‐average molecular weight, from reactor process variables. The subsequent integration of the material balances allowed to obtain the time evolution of conversion and , along the polymerization process. The accuracy of the proposed model under a wide range of conditions was assessed. The low computer‐time load makes the hybrid model suitable for optimization strategies.
The objective of this work was to develop a model for an extractive ethanol fermentation in a simple and rapid way. This model
must be sufficiently reliable to be used for posterior optimization and control studies. A hybrid neural model was developed,
combining mass and energy balances with neural networks, which describe the process kinetics. To determine the best model,
two structures of neural networks were compared: the functional link networks and the feedforward neural networks. The two
structures are shown to describe well the process kinetics, and the advantages of using the functional link networks are discussed. 相似文献
An adaptive control scheme is developed for the optimization of a fed-batch ethanol production process. The fermentation process
is modeled by an hybrid neural model combining mass balance equations and neural networks, used to represent the kinetic rates.
The networks used, the functional link networks (FLN), allow the linear estimation of their parameters; this enables the re-estimation
of the parameters at each sampling time, and thus the development of an adaptive optimal control scheme. 相似文献
Cephalosporin C production process withCephalosporium acremonium ATCC 48272 in synthetic medium was investigated and the experimental results allowed the development of a mathematical model
describing the process behavior. The model was able to explain fairly well the diauxic phenomenon, higher growth rate during
the glucose-consumption phase, and the production occurring only in the sucrose-consumption phase.
Moreover, the process was simulated utilizing the neural-networks technique. Two feed-forward neural-networks with one hidden
layer were employed. Both models, phenomenological and neural-networks based, satisfactorily describe the bioprocess. The
difficulties in determining kinetic parameters are avoided when neural networks are utilized. 相似文献
Novel nanoporous thermosetting films were obtained from thermostable polycyanurate (PCN)-based hybrid networks synthesized by polycyclotrimerization of cyanate ester of bisphenol E in the presence of a modifier reactive toward cyanate groups, i.e. dihydroxy-telechelic poly(ε-caprolactone) (PCL). The nanoporous structure was generated in PCN/PCL hybrid networks after extraction of unreacted free PCL sub-chains which were not chemically incorporated into the PCN cross-linked framework. Structure–property relationships for precursory and porous PCN/PCL hybrid networks were investigated using a large array of physico-chemical techniques. The porosity associated with the networks after extraction was more particularly evaluated by SEM and DSC-based thermoporometry: pore sizes around 10–90 nm were determined along with pore volumes as high as about 0.3 cm3 g−1. Density and dielectric measurements strongly suggested the occurrence of closed pore structures. Due to their high thermal stability as investigated by TGA, nanoporous PCN/PCL hybrid cross-linked films could be considered as promising materials for potential applications as thermostable membranes. 相似文献
In this work a procedure for the development of a robust mathematical model for an industrial alcoholic fermentation process was evaluated. The proposed model is a hybrid neural model, which combines mass and energy balance equations with functional link networks to describe the kinetics. These networks have been shown to have a good nonlinear approximation capability, although the estimation of its weights is linear. The proposed model considers the effect of temperature on the kinetics and has the neural network weights reestimated always so that a change in operational conditions occurs. This allow to follow the system behavior when changes in operating conditions occur. 相似文献
One serious difficulty in modeling a fermentative process is the forecasting of the duration of the lag phase. The usual approach
to model biochemical reactors relies on first-principles, unstructured mathematical models. These models are not able to take
into account changes in the process response caused by different incubation times or by repeated fed batches. Toover come
this problem, we have proposed a hybrid neural network algorithm. Feedforward neural networks were used to estimate rates
of cell growth, substrate consumption, and product formation from on-line measurements during cephalosporin C production.
These rates were included in the mass balance equations to estimate key process variables: concentrations of cells, substrate,
and product. Data from fed-batch fermentation runs in a stirred aerated bioreactor employing the microorganism Cephalosporium acremonium ATCC 48272 were used. On-line measurements strongly related to the mass and activity of the cells used. They include carbon
dioxide and oxygen concentrations in the exhausted gas. Good results were obtained using this approach. 相似文献
A first‐principles mathematical model for emulsion polymerization was reduced by using a hybrid mathematical model composed by artificial neural networks (ANN) and material balances. The goal was to have an accurate model that may be integrated fast enough to be used for online optimization purposes. In the reduced model the polymerization rate and the instantaneous weight‐average molecular weight were calculated by means of artificial neural networks. These ANNs were incorporated to first‐principles material balances. The accuracy of the reduced model under a wide range of conditions was assessed. Savings in computer time were achieved by using the reduced model, which makes it suitable for online optimization purposes.
Effect of the temperature on the cumulative weight‐average molecular weight: first principles mathematical model (—); (ANN2) and hybrid model predictions: (▵) 50 °C, (▪) 60 °C(training), (▿) 70 °C(validation), (•) 80 °C, (○) 90 °C. 相似文献
The temperature and pH effects on the equilibrium of a blood plasma model have been studied on the basis of artificial neural
networks. The proposed blood plasma was modeled considering two important metals, calcium and magnesium, and six ligands,
namely, alanate, carbonate, citrate, glycinate, histidinate and succinate. A large data set has been used to simulate different
concentrations of magnesium and calcium as a function of temperature and pH and these data were used for training the neural
network. The proposed model allowed different types of analyses, such as the effects of pH on calcium and magnesium concentrations,
the competition between calcium and magnesium for ligands and the effects of temperature on calcium and magnesium concentrations.
The model developed was also used to predict how the variation of calcium concentration can affect magnesium concentrations.
A comparison of neural network predictions against experimental data produced errors of about 3%. Moreover, in agreement with
experimental measurements (Wang et al. in Arch. Pathol. 126:947–950, 2002; Heining et al. in Scand. J. Clin. Lab. Invest. 43:709–714, 1983), the artificial neural network predicted that calcium and magnesium concentrations decrease when pH increases.
Similarly, the magnesium concentrations are less sensitive than calcium concentrations to pH changes. It is also found that
both calcium and magnesium concentrations decrease when the temperature increases. Finally, the theoretical model also predicted
that an increase of calcium concentrations will lead to an increase of magnesium concentration almost at the same rate. These
results suggest that artificial neural networks can be efficiently applied as a complementary tool for studying metal ion
complexation, with especial attention to the blood plasma analysis.
Figure Artificial neural networks for predicting the behavior of calcium and magnesium as a function of pH and temperature in human
blood plasma 相似文献
Process identification for composting of tobacco solid waste in an aerobic, adiabatic batch reactor was carried out using
neural network-based models which utilized the nonlinear finite impulse response and nonlinear autoregressive model with exogenous
inputs identification methods. Two soft sensors were developed for the estimation of conversion. The neural networks were
trained by the adaptive gradient method using cascade learning. The developed models showed that the neural networks could
be applied as intelligent software sensors giving a possibility of continuous process monitoring. The models have a potential
to be used for inferential control of composting process in batch reactors.
Presented at the 33rd International Conference of the Slovak Society of Chemical Engineering, Tatranské Matliare, 22–26 May
2006. 相似文献
Azobenzene-containing TiO2/γ-glycidoxypropyltrimethoxysilane and methyltrimethoxysilane hybrid films are prepared by combining a low temperature sol–gel
process and a spin-coating technique. The trans–cis isomerization of azobenzene small molecules inside organic–inorganic hybrid films is induced by a photoirradiation under
UV light. It is noted that below a baking temperature of 150 °C is necessary for the hybrid films in optical storage or optical
switching applications. The change of the refractive index and thickness of the hybrid films with real-time heating temperature
are observed by using a prism coupling technique. The structural properties of the hybrid films are also characterized and
investigated by thermal gravimetric analysis, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy.
The results indicate that the as-prepared hybrid films might allow directly integrating the optical storage or optical switching
devices with the waveguide devices on the same chip. 相似文献
A set of materials has been prepared by sol–gel process containing different quantities of hydroxyapatite (0, 2.5 and 5% HAp
w/w) using as silica precursors glycidyloxypropyltrimethoxysilane (GPTMS) and triethoxyvinylsilane (VTES). In order to optimize
the curing process to obtain sintherized systems (inorganic network) or hybrid systems (organic–inorganic) a TG and FTIR studies
have been developed and degradation kinetic triplet parameters were obtained (the activation energy, pre-exponential factor,
and function of degree of conversion). The kinetic study was analyzed by means of an integral isoconversional non-isothermal
procedure (model free), and the kinetic model was determined by the Coats–Redfern method and through the compensation effect
(IKR). All the systems followed the n = 6 kinetic model. The addition of HAp increases the thermal stability of the systems. The isothermal degradation was simulated
from non-isothermal data, and the curing process could be defined to obtain the two types of materials. Temperature under
250 °C allows the formation of hybrids networks. 相似文献