الفهرس 
Abstract
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Abstract
In this thesis, we mainly present the process of building the soft sensing model of CO2removal from syngas and of the level of Benfield solution (B.S) in regeneration tower in Urea plant. In Urea plant, some chemical processes takes place, in the stage of CO2 removal natural gas refoming and the syngas shift conversion which producese a large amount of carbon dioxide that need to be removed to make syngss suitable for Amonia unit . A Benfield hot carbonate system is used for carbon dioxide removal and recovery as by prooduct for use in Urea manufacture. The Raw synthesis gas which contains carbon dioxide is reduced in the system by the Benfield activated carbonate process at which the absorption at elevanted pressure, removes the CO2 by using an aquaeous solution of potassium carbonate. It is very important to construct a mathematical model which simulates perfectly thepractical system. The target of stabilizing the operation of the Benfield solution in (Talkha 2 Fertilizer Company) is designing appropriate control system for removal CO2 (carbon dioxide) system in the stage of (Benfield Solution) with making the level in this stage at desired point. Construction of such a model (C02 removal from syngas ) is not presented in references, so the objective of this work is to formulate and construct a suitable mathematical model, fuzzy model and neuro fuzzy model for this real system (production of CO2 in Benfield stage). In this thesis, a nonlinear model for Practical study of fluid system that includes Benfield Solution (B.S.) in urea plant system is presented. The present work measures the practical values of the quantity and level ofB.S. inside different containers of the process and study the dynamic behavior of that process by constructing a deterministic, lumped, parametric, continuous time model using transfer function and state space. This model is used to design a suitable control system. The experimental studies beside the theoretical analysis
are carried with proper results. Adaptive neural network based fuzzy inference system(ANFIS) as an intelligent neuro -fuzzy technique used for modeling system and a given practical training data set is partitioned into a set of clusters based on subtractive clustering method. The controller inputs are the level error and its derivative for the head of de-absorption tower part of the controller for the automatic leve! regulation. These are readily measurable quantities leading to a simple controller which can be easily implemented. It is shown that by