Oct-2010

Online real-time optimisation helps identify energy gaps

Real-time supervision of a refinery’s energy system enables optimisation of its fuel consumption and emissions levels

Marcelo Galleguillos and Michel Maffet, ENAP Refinerías
Marcos Kihn, Rubén Monje and Carlos Ruiz, Soteica

Article Summary

Refining and petrochemical industries operate complex energy systems based on several steam pressure levels, with fired boilers producing high- pressure steam and burning different fuels, and cogeneration units supplying steam and electricity to the site. The use of different fuels dealing with fuel gas production/consumption imbalances, electricity imports/exports with their corresponding commercial contracts, and finally having to comply with many environmental constraints provides several economic trade-offs to operate the site-wide energy system at minimum cost.

Founded in 2005 as a result of the merger of Aconcagua and Bío Bío refining plants, ENAP Refinerías supplies more than 80% of the fuel requirements in Chile and also exports its production to Peru, Ecuador and Central America, complying with the most demanding standards in the world. Aconcagua refinery has a crude oil distillation capacity of 100 000 b/d. Its main units are, among others: the fractioning area (which includes topping and vacuum units, visbreaking, Merox and solvents plant); the cracking area (FCC, alkylation, SRU, LPG treatment, isomerisation plant); the hydrogen area (continuous reforming, MHC, HCK, HDT), DCU; and the utilities supply area.

Aconcagua Refinery undertook an initiative to deploy a site-wide energy management system (EMS) tool for assisting managers and operators in the daily decisions they need to make for management of the utilities system.

Soteica’s Visual MESA Software, an industrial online, real-time EMS, was implemented with the purpose of helping Aconcagua refinery to reduce the operating costs of its utilities system. The software model developed not only gives recommendations on how to optimally manage the steam and electrical network, but also calculates energy-related key performance indicators (KPI), helping to identify operational gaps and emissions of both CO2 and SO2. The model can be used in standalone mode to perform case studies for economical evaluations of potential investments and for planning the operation of the utilities system.

The energy system, the steps for the implementation of Visual MESA and several features of the model are described in this article, with a focus on the use of the software for the calculation of energy-related KPIs. The EMS implementation project is discussed and the main conclusions relative to the reduction in operating costs are also presented.

Project schedule
The implementation of the energy management system was completed in 12 months. The main project stages were:
• Information requests Once the purchase order was issued, information on the utilities system was requested to the site in order to build an accurate model. A document including all the questions was submitted to the project owner, who provided the necessary information and coordinated all the steps throughout the project
• Kick-off meeting After reviewing all the information, an on-site meeting took place to clarify additional questions with the project owner. The optimisation strategy was discussed
• Software installation Visual MESA was licensed and configured in the PC that would work as a server. The connection between both the software and the OPC server was also configured. Remote access was granted during this stage and was used throughout the rest of the project
• Functional design specification document A functional design specification document was prepared, revised by both parties in concert and then approved by the site
• Visual MESA model building and energy-related KPIs calculations At this stage, a detailed model of the energy system (fuels, steam, electricity, BFW and condensates network) and the Excel report were built, working on the server by using the remote desktop connection made available in one of the previous stages. Sensor blocks were added into the model to use the online real-time and historical data. Energy-related KPIs calculations were defined and programmed
• Optimisation configuration A second trip to the site’s facilities was made during this stage and used for reviewing the model as well as the optimisation strategy. Upon model approval, a whole month of testing was performed. Although the model was run automatically, the recommendations were not yet followed by operators. This allowed us to build a base line for the predicted savings that could be obtained at this stage
• Optimisation start-up After the operators and engineers were trained, the recommendations started to be followed on a routine basis and the energy gaps were identified using the defined KPIs. The project documentation was provided and a benefits report was submitted.

Visual MESA model optimisation variables
The energy system of Aconcagua refinery is based on four steam pressure levels. It includes five boilers, which produce high-pressure steam, and a set of turbogenerators producing electricity. A main view of the model can be seen in Figure 1. The site also operates dual heaters, which can burn fuel oil and fuel gas. Several potentially swappable electric/steam driver pairs and groups were identified and included.

The built model was executed automatically and was continually fed with online, validated, real-time data. Its purpose was to produce recommendations in order to reduce the operative cost of the utilities system, respecting operational constraints such as minimum and maximum steam production, electrical power consumption limits and so on. In addition, the model could be used to perform case studies and evaluate the economic impact of potential investments and/or for planning the operation of the energy system in a given scenario.

The economic trade-offs between electrical power, steam and fuel networks made it a difficult task to manage the utilities system in order to operate it at minimum cost. SO2 and NOx emission flows were taken into account in the model as part of the optimisation constraints, so no operative variable should be pushed to a point where these constraints are not met. Particularly in the case of emissions, they need to be calculated for monitoring, tracking and reduction in accordance with the Kyoto protocol.

Optimisation variables were chosen among those where there is some freedom for manipulation during normal operation conditions. An example of continuous variables can be found in the steam production rate or the flow of fuels burnt in a boiler; there is free choice as long as the total steam demand is satisfied and the emission limits are respected. Examples of discrete variables are the swappable turbine or operation of the motor drives.