Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology

Depósito de Investigación de la Universidad de Sevilla https://idus.us.es/ This is an Accepted Manuscript of an article published by Elsevier in Fuel Processing Technology, Vol. 103, on November 2012, , available at: https://doi.org/10.1016/j.fuproc.2011.07.010 © 2011 Elsevier. En idUS Licencia Creative Commons CC BY-NC-ND UNCORRECTED PROOF 1Improving energy efficiency in a naphtha reforming plant using 2Six Sigma methodology 3R. González Falcón ⁎, D. Velázquez Alonso 1 , L.M. Gallego Fernández 2 , Luis Pérez-Lombard 3 4Universidad de Sevilla, Escuela Técnica Superior de Ingenieros de Sevilla, ESI Camino de los Descubrimientos s/n 41092, Seville, Spain 5 6 abstractarticle info 7Article history: 8Received 30 June 2011 9Accepted 13 July 2011 10 Available online xxxx 1112 13 14 Keywords: 15 Improve energy efficiency 16 Six Sigma 17 Distillation unit 18 Naphtha reforming 19The distillation process accounts more than 25% of total process energy consumption in refineries. Therefore 20the energy improvements of the distillations units are very important for this sector. Six Sigma is a 21methodology supported by a handful of powerful statistical tools in order to reduce variation through 22continuous process improvement. This paper proposed the application of Six Sigma methodology for 23improving energy efficiency in a distillation unit of a naphtha reforming plant. The results reproduce the past 24energy performance of the unit through multivariate models and show optimal operation mode with an 25expected savings around 150,000€/year. Although the process may further improve optimizing the 26consumption of the reaction unit. 27© 2011 Published by Elsevier B.V. 2829 30 31 32 1. Introduction 33 Industry uses more energy than any other end-use sector, 34 consuming about one-half of the world's. The industrial sector 35 comprised 51% of global delivered energy use in 2007 and grows by 36 an average annual 1.3%over the projection [1]. Approximately half of 37 all industrial energy use is used in specific processes in the energy- 38 intensive industries [2]. Opportunities and potentials exist for energy 39 savings through energy efficiency improvement in all sectors and 40 countries. 41 Petroleum refining is the most energy-intensive manufacturing 42 industry, for example According to the most recent Manufacturing 43 Energy Consumption Survey (MECS) conducted by the Energy 44 Information Administration (EIA) of the U.S. Department of Energy 45 (DOE), the U.S. petroleum refining industry accounts for about 7% of 46 total U.S. energy consumption [3]. 47 Energy consumption in refineries is dominated by a few processes 48 that, while not the most energy-intensive, have the greatest 49 throughput. For example, the distillation process accounts more 50 than 25% of total process energy consumption [3]. A distillation unit is 51 generally and widely utilized in chemical and petroleum industries for 52 separation of mixtures characterized by boiling points. A distillation 53unit (DU) is one of the critical and important unit operations for 54petroleum industry. The operating goal of a DU is to achieve well- 55controlled and stable system, high production rate and product 56quality as well as low operating cost for the economic consideration. 57Therefore, the engineering design, control strategy and process 58optimization of a DU has been paid attention to improve product 59efficiency and quality assurance in petroleum industry in recent years 60[4]. 61Six Sigma is a well-structured methodology that focuses on 62reducing variation, measuring defects and improving the quality of 63products, processes and services [5,6]. Six Sigma was initially 64introduced in manufacturing processes; today, however, marketing, 65purchasing, billing, invoicing, insurance, human resource and cus- 66tomer call answering functions are also implementing the Six Sigma 67methodology with the aim of continuously reducing defects through- 68out the organization's processes. 69The Six Sigma approach has been widely used to improve 70performances and reduce costs for several industrial fields over the 71past 10 years [7–10]. Many industries have applied Six Sigma 72methodology for analyze and improve energy management areas 73[11–14]. 74In this study the Six Sigma Define, Measure, Analyse, Improve and 75Control (DMAIC) methodology has been applied for improving energy 76efficiency in a DU of a naphtha reforming plant. The aim is to 77understand the application of the Six Sigma in the distillation process 78and its contributions to minimize the energy consumption. 79The object is to identify the critical input variables that have much 80influence on energy efficiency of distillations units. With the available 81historical data of the plant the goal is to reproduce the past behavior 82through multivariate models and to propose optimal operation mode 83from the models. Fuel Processing Technology xxx (2011) xxx–xxx ⁎Corresponding author at: Department of Energy Engineering, School of Industrial Engineering, University of Seville, Camino de los Descubrimientos, s/n 41092 Sevilla, Spain. Tel./fax: +34 954 487259. E-mail addresses: [email protected] (R.G. Falcón), [email protected] (D.V. Alonso), [email protected] (L.M.G. Fernández), [email protected] (L. Pérez-Lombard). 1 Tel.: +34 954 487253. 2 Tel.: +34 954 487256. 3 Tel.: +34 954 488131. FUPROC-03182; No of Pages 7 0378-3820/$ –see front matter © 2011 Published by Elsevier B.V. doi:10.1016/j.fuproc.2011.07.010 Contents lists available at ScienceDirect Fuel Processing Technology journal homepage: www.elsevier.com/locate/fuproc Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 84 The structure of this article is as follows. Section 2 explains an 85 introduction to the case study. The research methodology adopted for 86 this study is explained in section 3.Section 4 provides information 87 about the results obtained on each methodology phases. Finally 88 section 5 presents the concluding remarks and discusses the benefits 89 and limitations of the study. 90 2. Naphtha reforming plant 91 2.1. Process description 92 Naphtha reforming plant are composed of many different 93 operating units that are used to separate fractions, improve the 94 quality of the fractions and increase the production of higher valued 95 products like gasoline, jet fuel, diesel oil and home heating oil. 96This paper pays attention to the platforming unit (PU). This unit is 97constituted for two basic units: The catalytic reforming or reaction 98unit and the distillation unit (DU) or train distillation. 99The catalytic reforming of naphtha is an important refining process 100that seeks to improve the octane number of gasoline due to a 101conversion to paraffins and naphthenes in aromatics (Fig. 1). The feed 102to the naphtha reformer is a crude oil fraction from the refinery crude 103unit with a boiling range of 100–180 °C. This process is adiabatically 104carried out in three reformers, at high temperatures, building up 105gasoline with high octane number, LPG, hydrogen, fuel gas and coke. 106The effluent from the last reactor is cooled partly by heat exchange 107with the reactor charge. The stream then enters the product separator 108where flash separation of hydrogen (in recycle gas stream containing 10960–90 mol%hydrogen), and some of the light hydrocarbons takes 110place. The separator liquid, also called ‘reformate’, containing desired 111reformate, is pumped to the distillation unit. The function of the Fig. 1Q4 .Process flow diagram of the catalytic reforming plant. Fig. 2. Process flow diagram of the DU. 2R.G. Falcón et al. / Fuel Processing Technology xxx (2011) xxx–xxx Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 112 distillation unit is to separate the ‘reformate’to produce the aromatic 113 fraction, i.e., benzene, toluene, C8- and C9-aromatics. 114 This separate of product are make in different distillation column 115 (Fig. 2). The first column, debutanizer, is feed to the separator liquid 116 and a stream, called ‘aromatic LPG’, from external PU. This column 117 fraction to the fed in two basic products: butane, to the top and high 118 hydrocarbon flow, also called ‘platformer’, to the bottom column. 119 The product bottom is the fed to the debencenizer, the second 120 distillation unit. Its goal is to obtain a light aromatic flow to the top 121 free to the high hydrocarbon. This stream is the fed to the third 122 distillation column where the products are benzene and toluene. 123 Benzene and toluene are the most important product of the plant. 124 These products are sent to the Morphylane Unit. The bottom product 125 of the second column and the top product of the third column are 126 stored up or sent to the other units of the refinery. 127 2.2. Energy requirements 128 The majority of the process energy input to platforming unit is in 129 the form of fuels or steam used in process heaters for DU feed or 130 interstage heating and catalyst regeneration. Electricity is used to 131 power compressors in the separator, pumps, and other auxiliary 132 equipment. 133 The major energy consumers of the PU are the three reactors and 134 the first two columns of the DU. Process energy used for the PU is 135 presenting in Table 1Q1 . 136 2.3. Process variables 137 The Table 2 describes data available from the process. Include a 138 total of forty variables, eight of which correspond to operating 139 conditions of the distillation units and the remaining variables of 140 instrumentation. The location of these variables in plant is shown in 141 Fig. 3. 142 3. Methodology 143 Fig. 4 shows the key steps using DMAIC methodology [5,6] and the 144 tools used in each phase of study. The first phase defines the project 145 goals and customer requirement. The phase one measures the process 146 to determine current performance. The next phase analyzes and 147 determines the critical input variables for process improvement. 148 Phase three improve the process eliminating wasteful causes. And the 149 last phase control de improved process performance. 150 4. Results 151 4.1. Define phase 152 The analysis of the plant is critical to the development of the 153 methodology. The process flow diagrams and manuals supplied plant 154 are analyzed and studied in detail to understand any effect on the 155 change in operating conditions. 156 The subject of this case study is the platforming unit, which is 157 major energy consumer of the reforming plant. Table 1t1:1 Energy used of main equipment of the PU. t1:2 t1:3Energy source Unit/equipment Annual energy used (MWh/year) t1:4Fuel gas RU/reactors 155.440 t1:5DU/desbutanizer 37.400 t1:6Medium pressure steam DU/desbencenizer 17.050 t1:7Low pressure steam DU/despentanizer 12.970 Table 2 t2:1 Process variables description. t2:2 t2:3Id. SCD Variables Units Description t2:41 T input PP_V5 °C Input temperature PP_V5 reactor t2:52 T input PP_V6 °C Input temperature PP_V6 reactor t2:63 T input PP_V7 °C Input temperature PP_V7 reactor t2:74 T output PP_V5 °C Output temperature PP_V5 reactor t2:85 T output PP_V6 °C Output temperature PP_V6 reactor t2:96 T output PP_V7 °C Output temperature PP_V7 reactor t2:107 P PP_V8 bar Pressure in the PP_V8 separator t2:118 Recycle gas density kg/Nm 3 Recycle gas density its controller to the separator pressure (P_PP_V8) t2:129 Platforming flow m 3 /h Input stream to the platforming unit t2:13–T ambient °C Ambient temperature t2:1441 T DELTA PP_V5/6/7 °C Jumper between reactor temperature t2:1510 LPG Aromax m 3 /h Input stream from Aromax unit to debutanizer (this unit is out to the volume control) t2:1611 Wait platforming °C This variable explain the degradation catalyst t2:1712 F PP-V8 a PP-V11 m 3 /h Output stream from PP_V8 separator (bottom stream to the separator) to debutanizer t2:1813 F PP_V11 m 3 /h Input stream to debutanizer t2:1914 F LPG GAS (Nm 3 /h) m 3 /h Gas fraction of the debutanizer's top flow t2:2015 F LPG LIQ (m 3 /h) m 3 /h Liquid fraction of the debutanizer's top flow t2:21–DT_ PP_V5 °C Temperature jumper in the PP_V5 reactor t2:22–DT_ PP_V6 °C Temperature jumper in the PP_V6 reactor t2:23–DT_ PP_V7 °C Temperature jumper in the PP_V7 reactor t2:2441 DT_total °C Jumper between reactor temperatures. It is the sum temperature jumper of each reactor t2:2516 F FG AL PP-H3 m 3 /h Gas fuel consumption in the PP_H3 furnace t2:2617 F FG AL PP-H4 m 3 /h Gas fuel consumption in the PP_H4 furnace t2:2718 F FG AL PP-H5 m 3 /h Gas fuel consumption in the PP_H5 furnace t2:2819 F FG AL PP-H6 m 3 /h Gas fuel consumption in the PP_H6 furnace (debutanizer's reboiler) t2:2920 VM A PP-E16 kg/h Low pressure steam consumption in the reboiler that hot exchange with F PP_V11 t2:3021 Flow to debenzenizer m 3 /h It is the same that debutanizer's bottom stream t2:3122 Top flow debenzenizer m 3 /h Gas fraction of the debenzenizer's top flow t2:3223 Bottom flow debenzenizer to YT- 135 m 3 /h Liquid fraction of the debenzenizer's bottom flow to the tank YT_135 t2:3324 Bottom flow debenzenizer to Gasoline m 3 /h Liquid fraction of the debenzenizer's bottom flow to the gasoline production t2:3425 VM A PP-E20 kg/h Medium pressure steam consumption in the debenzenizer's reboiler t2:3526 Top flow depentenizer m 3 /h Gas fraction of the depentenizer's top flow t2:3627 Bottom flow depentenizer m 3 /h Liquid fraction of the depentenizer's bottom flow t2:3728 VM A SE-E13 kg/h Medium pressure steam consumption in the depentenizer's reboiler t2:3829 RR debuta Reflux ratio: Total of debutanizer's top stream/liquid return to the column t2:3930 RR debenze Reflux ratio: Total of debenzenizer's top stream/liquid return to the column t2:4031 RR depenta Reflux ratio: Total of depentenizer's top stream/liquid return to the column t2:4132 P_V13 bar Debenzenizer's pressure operation t2:4233 T_top_V13 °C Top gasses output temperature of the debenzenizer t2:4334 T_bottom_V13 °C Bottom liquid output temperature of the debenzenizer t2:4435 P_V11 bar Debutanizar's pressure operation t2:4536 T_top_V11 °C Top gasses output temperature of the debutanizer t2:4637 T_bottom_V11 °C Bottom liquid output temperature of the debutanizer t2:4738 P_V12 bar Depentenizer's pressure operation t2:4839 T_top_V12 °C Top gasses output temperature of the depentenizer t2:4940 T_bottom_V12 °C Bottom liquid output temperature of the depentenizer 3R.G. Falcón et al. / Fuel Processing Technology xxx (2011) xxx–xxx Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 158 4.1.1. Objective 159 This project goal is to improve the energy efficiency of the 160 distillation process in order to reduce energy consumption of fuel gas 161 (FG) of debutanizer and medium steam (MS) of debenzenizer. 162 In this phase, the energy efficiency indicators of the process are 163 defined (Table 3). 164 4.2. Measurement phase 165 4.2.1. Process block step 166 Using the process block step, are identified four steps in the 167 process: Reaction unit, Debutanizer, Debencenizer and Depentenizer. 168 Identify all steps in the process graphically. 169 Variables have been identified in the area of Platforming making a 170 distinction between inputs and outputs of each step (Fig. 5). 171 4.2.2. Key input variables 172 It assessed the influence of the inputs identified in the previous 173 phase on energy efficiency by developing a Cause and Effect Matrix. 174 From the Pareto chart resulting will select the most influential 175 variables (Table 4). 1764.3. Analysis phase 1774.3.1. Modeling for EEI 178It has been identified through the use of graphical matrix and 179stepwise regression, a total of 14 variables with a critical influence on 180the IEE (Table 5). 181For this variable were obtained the following models of IEE: IEEFG =−4:78−0:0368·Platf orming Flow +0:255·P PPV8 +0:0793·RRdebuta +0:0372·T bottom V11 −0:0867·DT PP V7+0:269·PV11 + 11·KPIdebuta ð1Þ Fig. 3. Process variables location. Phase I: Phase Items Tools Measurement Phase 0: Define Establish the characteristics of the process Identify inputs Block step C&E Matrix SICOP Capacity analysis Pareto Diagram Phase II: Analysis Exploratory analysis Identity Potential Critical Inputs Modelling of the indicators (Y's) Description analysis Define Y’s (customer requirements) Analysis of the ability to process improvement Optimization of the column (debutanizer): optimal operation conditions for minimum consumption Perform Multi-vari Analysis Stepwise regression Multi-vari lineal regression SOLVER tool Capacity analysis Phase III: Improvement Analyze of improve of the process Capacity analysis Phase IV: Control Implement Control Plan Verify Long Term Capability Continuously Improve Process Fig. 4. Phases of Six Sigma DMAIC methodology. Table 3 t3:1 Energy efficiency indicator for fuel gas (FG) and medium steam (MS) consumption. t3:2 t3:3Distillation unit Energy efficiency indicator Units t3:4Debutanizer EEIFG =FG Consumption Platf orming Flow m 3 FG/m 3 PF t3:5Debenzenizer EEIMS =MS Consumption Platforming Flow kg MS/m 3 PF 4R.G. Falcón et al. / Fuel Processing Technology xxx (2011) xxx–xxx Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 182183 IEEMS =−254−0:901·FPPV8toPPV11 + 2:28·TbottomV13 +0:643·TtopV13 + 172·KPIdebenz +49·RRdebenze −146··KPIdebuta ð2Þ 184185Where: KPIdebuta =1:73−0:593·recycle gas density +0:012·LPG Aromax−0:00160·WAIT +0:00129·DT PP V7 KPIdebenze =0:415 + 0:0183·LPG Aromax−0:000964·DTtotal −0:00121·Platforming Flow−0:00396·DT PP V7 186187 188 The models allow obtaining the operating conditions of the 189columns that make minimum energy consumption in these devices. 190An optimization analysis of the columns allows to know the value 191being the desired objective. 192Obtained models, they are validated with data from a different 193time period modeled (Fig. 7Q2). Errors obtained from these models are 194less than 2%. Inputs Inputs Inputs Inputs T input PP_V5 T ambient T ambient T ambient T input PP_V6 LPG Aromax Flow to debenzenizer Top flow debenzenizer T input PP_V7 F PP-V8 a PP-V11 P_V13 P_V12 T output PP_V5 F PP_V11 RR desbence RR despenta T output PP_V6 RR desbuta T_top_V13 T_bottom_V12 T output PP_V7 P_V11 T_bottom_V13 T_top_V12 P PP_V8 T_top_V11 Recycle gas density T_fond_V11 Platforming Flow DT PP_V5/6/7 Recycle gas density DT_ PP_V5 DT_ PP_V6 DT_ PP_V7 Reaction Unit Debutanizer Debencenizer Depentenizer Outputs Outputs Outputs Outputs F FG AL PP-H3 F FG AL PP-H6 VM A PP-E20 VM A PP-E20 F FG AL PP-H4 F FG AL PP-H5 F PP-V8 a PP-V11 Fig. 5. Block step platforming unit. Table 4t4:1 List key input variables of the process. t4:2 t4:3Id. SCD Variables Units t4:41 T input PP_V5 °C t4:52 T input PP_V6 °C t4:63 T input PP_V7 °C t4:74 T output PP_V5 °C t4:85 T output PP_V6 °C t4:96 T output PP_V7 °C t4:10 7 P PP_V8 bar t4:11 8 Recycle gas density kg/Nm 3 t4:12 9 Platforming Flow m 3 /h t4:13 10 LPG Aromax m 3 /h t4:14 11 Wait platforming °C t4:15 12 F PP-V8 a PP-V11 m 3 /h t4:16 –DT_ PP_V5 °C t4:17 –DT_ PP_V6 °C t4:18 –DT_ PP_V7 °C t4:19 41 DT_total °C t4:20 19 F FG AL PP-H6 m 3 /h t4:21 21 Flow to debenzenizer m 3 /h t4:22 22 Top flow debenzenizer m 3 /h t4:23 25 VM A PP-E20 kg/h t4:24 29 RR debuta t4:25 30 RR debenze t4:26 32 P_V13 bar t4:27 33 T_top_V13 °C t4:28 34 T_bottom_V13 °C t4:29 35 P_V11 bar t4:30 36 T_top_V11 °C t4:31 37 T_bottom_V11 °C Table 5 t5:1 List critical input variables of the process. t5:2 t5:3Id. SCD Variables Units t5:47 P PP_V8 bar t5:58 Recycle gas density kg/Nm 3 t5:69 Platforming flow m 3 /h t5:710 LPG Aromax m 3 /h t5:811 Wait platforming °C t5:912 F PP-V8 a PP-V11 m 3 /h t5:10–DT_ PP_V7 °C t5:1141 DT_total °C t5:1229 RR debuta t5:1330 RR debenze t5:1433 T_top_V13 °C t5:1534 T_bottom_V13 °C t5:1635 P_V11 bar t5:1737 T_bottom_V11 °C 5R.G. Falcón et al. / Fuel Processing Technology xxx (2011) xxx–xxx Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 195 4.3.2. Capacity analysis: Set target 196 In this phase is developed a capacity analysis to analyze the 197 potential to improve the process set a consumption target. 198 This consumption target is specified by the customer. The ability to 199 present the process (operation with actual consumption data) to fit 200 the specified target is evident if we analyze the capacity parameters of 201 the process (Fig. 6). 202 The current FG consumption has normal distribution and the 203 current process capability reference is 1.33. 204 Cp rates recognize the consumption samples represent rational 205 subgroups, indicating how the process would behave if we could 206 eliminate the gradual and rapid changes between subgroups. Because 207 Cpk=0.97 is less than the reference makes the process of improving 208 this capability (Cpk=min{CPL, CPU}). 209 Moreover, Pp rates ignore subgroups and consider the general 210 variation of the process. This explains the overall change gradual and 211 rapid changes that can occur between subgroups and, therefore, is 212 useful in measuring the capacity over time. If its value differs 213 considerably from its value of Cp, we can conclude that there is 214 significant variation from one subgroup to another. 215Because Ppk value=0.85 is less than b1.33, the process is 216improved. This improvement will be achieved when we can reduce 217these sudden changes over time. 2184.4. Improvement phase 219In the improve phase checks whether the values of consumption 220optimized, from operating conditions of the DU, has really improved 221compared to baseline (real consumption). 222Although the parameters Cpk and Ppk are not close to the 223reference the values have improved significantly and that these 224intakes are close to the target set by the client, as well as away from 225the specification limits of operation (Cpm=0.91 vs. Cpm initial =0.78 Q3). 226But the process could further improve if improve other operating 227conditions, not just the operating conditions of the columns. 228Although the process may further improve (optimizing the 229consumption of the furnaces in the reaction zone), this improvement 230has been achieved without any investment. The results have been 231evaluated economically achieve savings of more than 157,000€/year. 330030002700240021001800 LIE Objetivo LSE LIE 1800 Objetivo 2660 LSE 3400 Medida de la muestra 2602,41 Número de muestra 54 Desv. Est. (Dentro) 275,238 Desv. Est. (General) 312,059 Procesar datos Cp 0,97 CPL 0,97 CPU 0,97 Cpk 0,97 Pp 0,85 PPL 0,86 PPU 0,85 Ppk 0,85 Cpm 0,78 Capacidad general Capacidad (dentro) del potencial PPM < LIE 0,00 PPM > LSE 0,00 PPM Total 0,00 Desempeño observado PPM < LIE 1776,63 PPM > LSE 1878,79 PPM Total 3655,42 Exp. Dentro del rendimiento PPM < LIE 5065,36 PPM > LSE 5295,57 PPM Total 10360,92 Exp. Rendimiento general Dentro de General Fig. 6. Capacity analysis of the FG consumption set target. 330030002700240021001800 LIE Objetivo LSE LIE 1800 Objetivo 2660 LSE 3400 Medida de la muestra 2657,53 Número de muestra 54 Desv. Est. (Dentro) 245,444 Desv. Est. (General) 271,604 Procesar datos Cp 1,09 CPL 1,16 CPU 1,01 Cpk 1,01 Pp 0,98 PPL 1,05 PPU 0,91 Ppk 0,91 Cpm 0,91 Capacidad general Capacidad (dentro) del potencial PPM < LIE 0,00 PPM > LSE 0,00 PPM Total 0,00 Desempeño observado PPM < LIE 238,09 PPM > LSE 1243,22 PPM Total 1481,31 Exp. Dentro del rendimiento PPM < LIE 796,21 PPM > LSE 3132,05 PPM Total 3928,26 Exp. Rendimiento general Dentro de General Fig. 7. Capacity analysis of the Optimum FG consumption set target. 6R.G. Falcón et al. / Fuel Processing Technology xxx (2011) xxx–xxx Please cite this article as: R.G. Falcón, et al., Improving energy efficiency in a naphtha reforming plant using Six Sigma methodology, Fuel Process. Technol. (2011), doi:10.1016/j.fuproc.2011.07.010 UNCORRECTED PROOF 232 5. Conclusions 233 It is found that the Six Sigma methodology is highly useful to 234 improve de energy efficiency of distillation units. The five-phase 235 DMAIC methodology is applied to characterize and optimize distilla- 236 tion process of a naphtha reforming unit. Organizing data into useful 237 information is accomplished in the measurement phase of the DMAIC 238 process, which establishes the baseline against which all future 239 improvements will be compared. 240 The analysis phase identified a total of critical inputs that can be 241 used to understand and correct process variation. Multivariate models 242 of the energy efficiency indicators have been obtained. These models 243 reproduce the past energy performance of the distillation units. 244 Operating modes that optimize the energy efficiency of the DU have 245 been proposed with an expected savings around 150,000€/year. 246 Furthermore the process may improve optimizing the consumption of 247 the furnaces in the reaction zone. 248 The results reached should be used in the future development of a 249 Control Plan. 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