Control of Thermodynamic Properties by Artificial Neural Networka ANNs

Fluid phase equilibria and mixing properties are of primary interest for theoretical purposes (mathematical model design, parameter estimatioii, etc.), and for the development of a general proper liquid theory. In chemical irrdrrstiial processes irrvolving liquid mixtures, the optimization and adequate design of separation 

Equipments are conditioned by a sufficient knowledge of nrixing theimodyrrarrrics (Iglesias et al., 2007). In what is referred to the unit operation field, the optirrrization of separation operations by extraction or distillation, require knowledge of the two-hquids phase eqitilibria, and thermodynamics, which can be determined either experimentally or by prediction based on an appropriate model, and a set of data. Artificial netual networks (ANNs) can also predict liqirid-liquid equilibrium (LLE) data as well as thermodynamic model and it dose not have the difficulties eqrration of state, EOS, model for obtain thermodynamic parameter. 

Although, EOS are derived based on strorrg physical principles, there is still certain amount of empiricism involved in terms of several adjrrstable parameters that are required in mixing ntles. Using EOS for estirrratmg the VLE is tediorrs arrd reqtrires an iterative method that may sometimes pose problem for real time. 

Control of an operating plant. In snch cases other faster alterrrative methods would be more attractive. The developmerrt of nrrmerical tools, such as ANN, has paved the way for alternative methods to estirrrate the LLE (Richardson et al., 2006). Although, 

The best example of a neural network is probably the hitman brain. In fact, the human brain is the most complex and powerful stracture known today. The ANNs are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. The ANN modeling is carried out in two steps the first step is to train the network whereas the second is to test the network with data, which were not used for training. The unit element of an ANN is the neuron (node). As in nature, the network function is determined largely by the coimections between the elements (Richardson et al., 2006). 

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J Modling and Control of Thermodynamic Properties by Artificial Neural Networka (ANNs)... 

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