Support System for consumption optimization in a aphtha reforming plant

In a naphtha distillation process, the natural objective is to perform an entire process maximization of the production rate while meeting required product qualities by searching for an optimal operating condition by manipulating the operating variables. The objective of this paper includes performing an energy process optimization. Not only is an adequate production rate met with the required product qualities but the operating cost is also minimized through a data mining approach. The study of the aphtha distillation Decision upport System Data mining ost optimization influence of all process attributes in the defined Energy Efficient Indicator (EEI) allows the construction of a multivariate linear model to aid human experts in the recovery of energy losses. A canonical discriminant function carried out the data prediction step. The quality of the Decision Support System framework is illustrated by a case study considering a real database. Also, a commercial software supported by this mining framework is presented. Introduction and bibliographical review The goal of simulating the performance of an expert is to help uman workers solve real-world problems by expertise, a specific omain of knowledge (Shiau, 2011). There are diverse problems hich need to be solved in the real world. Thus, the use of an expert ystem (or a similar artificial intelligence framework) becomes proific in many fields (Liao, 2005). One of the complex problems for the ontrol in which a computational intelligent approach is amenable s a crude oil distillation unit. In a crude distillation process, the first bjective is to perform an entire process optimization including igh production rate with a required product quality by searchng for an optimal operating condition of the operating variables Frenkel, 2011; Ouattara et al., 2012). In the previous decade, there as considerable research concerning the optimization of crude istillation processes (Ghashghaee & Karimzadeh, 2011). In Seo, h, and Lee (2000), the optimal feed location on both the main olumn and stabilizer is obtained by solving rigorous “a priori” odels and mixed integer nonlinear programming. The sensitivity o small variations in feed composition is studied in Dave, Dabhiya, atyadev, Ganguly, and Saraf (2003). Julka et al. propose in a twoart paper (Julka, Karimi, & Srinivasan, 2002; Julka, Srinivasan, ∗ Corresponding author. Tel.: +34 954 552836; fax: +34 954 552833. E-mail address: fbiscarri@us.es (F. Biscarri). & Karimi, 2002) a unified framework for modeling, monitoring and management of supply chain from crude selection and purchase to crude refining. In addition to analytical non-linear models, computational intelligence techniques such as neural networks (Gueddar & Dua, 2012; Liau, Yang, & Tsai, 2004) and genetic algorithms (Motlaghi, Jalali, & Ahmadabadi, 2008) are used for the same purpose. Alhajree, Zahedi, Manan, and Zadeh (2011) cite several Artificial Neural Network research studies for the control of processes in petrochemicals and refineries. From cited papers, most of the nonlinear controllers require the feedback of state information for effective control and close monitoring of a process. In practice, however, the complete online information about the present state of the industrial process is rarely available. If the real-world values are not provided to the algorithm on time, the control algorithm becomes formally invalid. In practice, it recovers from the situation, at the price of reduced quality control (i.e., worse product), so such situations should be avoided (Metzger & Polakow, 2011). The scope of this present study is concerned with a part of the crude oil distillation called the platforming unit. It is made up of two subunits: the catalytic reforming or reaction unit and the distillation unit or train distillation. Most of the cited references are focused on optimizing the production rate of the distillation unit (Iranshahi, Bahmanpour, Paymooni, Rahimpour, & Shariati, 2011; Meidanshahi, Bahmanpour, Iranshahi, & Rahimpour, 2011), but if the focus is the heat recovery, 80% of the energy consumption (67% of the energy invoicing tasks) corresponds to the fuel consumption in the boilers of the previous task (the reaction unit).