Decision system based on neural networks to optimize the energy efficiency of a petrochemical plant Iñigo Monedero a,⇑ , Félix Biscarri a , Carlos León a , Juan I. Guerrero a , Rocio González b , Luis Pérez-Lombard b a School of Computer Science and Engineering, Electronic Technology Department, Av. Reina Mercedes S/N, 41012 Seville, Spain b School of Industrial Engineering, Department of Energy Engineering, ESI Camino de los Descubrimientos S/N, 41092 Seville, Spain article info Keywords: Petrochemical plant Expert system Data mining Decision system Neural network Crude oil distillation Cost optimization abstract The energy efficiency of industrial plants is an important issue in any type of business but particularly in the chemical industry. Not only is it important in order to reduce costs, but also it is necessary even more as a means of reducing the amount of fuel that gets wasted, thereby improving productivity, ensuring better product quality, and generally increasing profits. This article describes a decision system devel- oped for optimizing the energy efficiency of a petrochemical plant. The system has been developed after a data mining process of the parameters registered in the past. The designed system carries out an opti- mization process of the energy efficiency of the plant based on a combined algorithm that uses the fol- lowing for obtaining a solution: On the one hand, the energy efficiency of the operation points occurred in the past and, on the other hand, a module of two neural networks to obtain new interpolated operation points. Besides, the work includes a previous discriminant analysis of the variables of the plant in order to select the parameters most important in the plant and to study the behavior of the energy efficiency index. This study also helped ensure an optimal training of the neural networks. The robustness of the system as well as its satisfactory results in the testing process (an average rise in the energy efficiency of around 7%, reaching, in some cases, up to 45%) have encouraged a consulting company (ALIATIS) to implement and to integrate the decision system as a pilot software in an SCADA. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction The applications of expert systems are rapidly increasing in the industry. Such applications are very effective in situations when the domain expert is not available (Shiau, 2011). There are diverse problems which need to be solved in the real world and they are difficult to solve by the expert at the moment of carrying out his work. Thus, the expert systems, and specifically the decision sys- tems, become prolific in many fields (Liao, 2005). On the other hand, data mining (Köksala, Batmazb, & Testikc, 2011), or the step of extracting knowledge from the databases, is a discipline inti- mately related to expert system and which makes it possible to ex- tract the necessary knowledge to design them. In chemical industry, one of the complex problems for the con- trol of which a computational intelligent approach is amenable, is a crude oil distillation unit. In a crude distillation process, the first objective is to perform an entire process optimization including high production rate with a required product quality by searching an optimal operating condition of the operating variables. In the previous decade, there was considerable research concerning the optimization of crude distillation process. In Seo, Oh, and Lee (2000), the optimal feed location on both the main column and sta- bilizer is obtained by solving rigorous ‘‘a priori’’ models and mixed integer nonlinear programming. The sensitivity to small variations in feed composition is studied in Dave, Dabhiya, Satyadev, Ganguly, and Saraf (2003). Julka et al. propose in a two-part paper (Julka, Karimi, & Srinivasan, 2002; Julka, Srinivasan, & Karimi, 2002) a uni- fied framework for modeling, monitoring and management of sup- ply chain from crude selection and purchase to crude refining. In addition to analytical non-linear models, computational intelli- gence techniques such as neural networks (Liau, Yang, & Tsai, 2004) and genetic algorithms (Motlaghi, Jalali, & Ahmadabadi, 2008) are used for the same purpose. In particular, neural networks have been used for modeling and estimation of processes in petro- chemical and refineries (Falla et al., 2006; Shirvani, Zahedi, & Bashiri, 2010; Zahedi, Parvizian & Rahimi, 2010). The scope of present study is concerned with a part of the crude oil distillation called the platforming unit. It is constituted of two subunits: the catalytic reforming or reaction unit and the distilla- tion unit or train distillation. The decision system is focused on optimizing the production rate of the distillation unit which is the most important zone of the platforming unit since it is the one that concentrates the consumption of the plant. At present, research is not focused only in the rise of the pro- duction rate (Jarullah, Mujtaba, & Wood, 2011; Meidanshahi, 0957-4174/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2012.02.165 ⇑ Corresponding author. E-mail address: imonedero@us.es (I. Monedero). Expert Systems with Applications 39 (2012) 9860–9867 Contents lists available at SciVerse ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa