Real Time Energy Management Some Case Studies

Visual MESA has all the required features to implement on-line optimization including:

• Sensor data easily tied to the model (drag and drop)
• Data validation
• Steady-state detection
• On-line model identification
• Control system interfaces for closed loop, on-line optimization
• Closed loop model and control system reliability and feasibility checks 

Optimization Variables and Constraints Configuration

Building a model that realistically represents the utilities and energy system topology, includes all the optimization variables and constraints and, at the same time, captures all the system economics, especially the fuels and electricity contractual complexity, is a straightforward task when Visual MESA is being used.

During model and optimization development, the following set of variables must be identified and properly configured:

Optimization Variables

Optimization variables are those variables where there exists a relatively free choice on what that value might be. For example, the steam production rate at which a particular boiler operates is a free choice as long as the total steam production is satisfied, thus each boiler flow can be optimized so that the most efficient boilers' production is maximized. There are two kinds of optimization variables that must be handled in optimizing a steam and electric system:

1. Continuous Variables: Such as steam production from a fired boiler or steam flow through a steam driven turbogenerator. It is also important to determine if the unit should be shutdown, recognizing its minimum operating limit.
2. Discrete Variables: Where the optimizer has to basically decide if a particular piece of equipment will operate or not. The most common occurrence of this kind of optimization in refinery steam systems is spared pump optimization where there is the need to choose which of two pumps to operate, one of which is driven by a steam turbine and the other by an electric motor. 

Constrained Variables

Constrained variables are those variables that cannot be freely chosen by the optimizer but must be limited for practical operation. There are two kinds of constraints to handle in steam and electrical system optimization:

1. Direct Equipment Constraints: An example of a direct equipment constraint is turbogenerator power output. In a turbogenerator, the steam that flows through the generator can be optimized within specified flow limits but there will also be a maximum power production limit. Another example is the emission limit (for example, on NOx, SO2, particulate material, CO2, etc.) related to the different fuels burned.
2. Abstract Constraints: An abstract constraint is one where the variable is not directly measured in the system or a constraint that is not a function of a single piece of equipment. An example of this type of constraint is a steam cushion (or excess steam production capacity). Steam cushion is a measure of the excess capacity in the system and provides a safety margin in case of an unexpected shutdown of a given steam generator. If this kind of constraint were not utilized, then an optimizer would usually recommend that the absolute minimum number of steam producers be operated. This is sometimes unsafe because the failure of one of the units could lead to the shut down of the entire facility. 

Typical Project Schedule

A typical project schedule includes the following main steps: 

• Kick-Off Meeting
• Control System Review
• Off-Site Model Building
• Mid-Point Review
• Burn-In Period
• Commissioning Visit 

Kick Off Meeting

During this step data collection is done. This includes the Piping and Instruments Diagrams, a tags list from the Real Time Database (plant information system) and equipment data sheets.

Software installation and connection to the Plant Information system is done at the beginning of the project.

Control System Review

A review of the steam, power, and fuel control systems with knowledgeable experts is performed. The goals of this Control System review are:

• Develop a list of variables already controlled and how Visual MESA needs to relate to them.
• Identify any needed new control strategies or changes to existing strategies to implement optimization.

Off-Site Model Building

1. As a result, the optimization suggestions can be achieved properly through the existing operating procedures and control strategies and structure.
2. A complete model of the overall Energy System is built. The model includes the whole fuel, steam, boiler feed water, condensate and electrical systems.
3. All the steam pressure levels are modeled as well as all the production units utilities network, with a high level of detail including all the consumers and suppliers to the respective steam, boiler feed water and condensate headers.
4. Electricity and fuel supply contract details are easily included in the model, with the electricity market cost updated from the real time data base system tags, when available.

The fuel gas network is usually also modeled, as it is involved with the steam and power generation equipment and all its constraints and degrees of freedoms are also taken into account by Visual MESA.

A well-tuned model would generally be run at Level 3, with a run at Level 4 once in a while to evaluate potential operational changes.

The objective function Visual MESA optimizes is the total operating cost of the system, which is:

Total Operating Cost = Total Fuel Cost + Total Electric Cost + Σ Miscellaneous Costs 

The SQP optimizer's job is to minimize this objective function subject to operating constraints in the system.

Total Fuel Cost is determined from the fuel use of each boiler and combustion turbine multiplied by their respective fuel prices.

Total Electric Cost is determined from the net electric use of each motor, load, and generator multiplied by their respective electric prices. The electric generation (power selling) is just negative electric use. The model can take into account the electricity price corresponding to the actual hour of the day as well as the penalties associated to selling more or less power than the market arranged exportation amount, if any.

Miscellaneous Costs are normally used to charge for demineralised water coming into the system, but can be used for any other cost related to the energy system (for example CO2 emission cost).

Calculations to model and optimize CO2 emission cost are also developed and run together with the electric, steam and fuel optimization and help choose the best fuel to use in boilers and gas turbines taking into account the emission costs. A standard feature of the software, the "constituents" can be used to model the emission produced by each type of fuel. 

Mid-Point Review

The model and optimization configuration is reviewed with users.

Training at engineering and users' level is performed, including the following items:

• Basic skills (model navigation and menus)
• Optimization
• Monitoring capabilities
• Case studies ("What If?" planning)
• Building models
• Building custom reports
• Software architecture 

Key Performance Indicators

In addition to the main purpose of the real time online optimization, appropriate performance metrics can also be identified and performance targets can be set during the real time energy management project. Also, within the model calculation and reporting infrastructure, corrective actions in the case of detected deviations from target performance can be recommended.

Those metrics are usually known as Key Performance Indicators (KPI's) and can be:

• High level KPIs that monitor site performance and are geared toward use by site and corporate management. For example: Total cost of the utilities system, predicted benefits, main steam headers imbalances, emissions, etc.
• Unit level KPIs that monitor individual unit performance and are geared toward use by unit management and technical specialists. For example: plant or area costs, boilers and heaters efficiencies, condensate recovery percentage, use of energy per unit of production, etc.

The metrics are intended to be used in a Site Monitoring and Targeting program where actual performance is tracked against targets in a timely manner, with deviations triggering a corrective response that results in savings.

Two of the main KPIs written back to the real time data base by Visual MESA are the Energy System economics and calculated efficiencies.

Each time the system is executed, the potential gap between the current operating the costs and the minimum operating costs is calculated. The savings associated with this gap will be materialized only if all the suggested optimization movements are implemented simultaneously. These predicted savings constitute one of the main economic KPIs.

It is expected that, if the operators implement all the proposed recommendations on a continuous basis, the potential gap will start decreasing over time. If in the future the system is in a new condition, a new potential gap will re-appear until the operators make the optimizing movements, and the cycle is repeated. If the projected savings are consistently high, this should prompt Management, Supervision and/or Engineering staff to identify any issue that is impeding the capture of the costs reduction.

Most commonly historized equipment efficiencies are: Boilers and heaters efficiency, Gas Turbines Heat Recovery Steam Generation systems (HRSGs) efficiencies, and Gas Turbines heat rate. Their long term trends help to identify those pieces of equipment that can lose efficiency over time and therefore justify cleaning, when possible.

Headers imbalances are also usually historized (the software automatically calculates the mass imbalances for each header where a special Visual MESA ad-hoc calculation block, called "balloon" is located).

Also, the headers imbalances are often related to sensor failures. A sudden increase of an imbalance would trigger the search for the cause. If a bad signal sensor or an out of range signal is identified, it would need to be repaired and or re-ranged. 

Sustainability Program

A proactive support program, which is underway for many of our clients, with the goal of ensuring that the benefits of the project are sustained and that the health of the model is kept in check over time is very important.

This advanced sustainability program is achieved by Soteica having remote access to the solution in order to conduct monthly audits of the models. Thanks to this program it has been possible to avoid the short term obsolescence that this type of systems usually suffers. Also, when personnel changes occur, Soteica acts as a transition back-up until the new resource is in place and trained avoiding discontinuity in the usage of the solution.

Under the Sustainability program the communication between the technology supplier and the end user becomes very fluid enabling the refinery personnel to develop the necessary skills in order to modify and revamp existing models on their own, as well as using the tool for planning and investment decisions via case studies.

When refiners identify that the Total Cost of Ownership, including the sustainability services, has paybacks of less than 6 months they concur that keeping advanced technologies such as Energy Real Time Optimization up and running and evergreen is a logical step. Not only is it profitable but it allows the refinery to operate at minimum energy cost and within emissions constraints, 24x7x365.

Conclusions

An on-line tool for industrial energy management has been presented, along with the experiences gained on several successful projects. Energy cost reductions have been obtained taking advantage of the Visual MESA software functionalities. It demonstrated to be a robust optimizer and very well suited to be used on-line, providing optimization recommendations to the operators on a routine basis.

The model was also used to evaluate a priori "What If?" scenarios that include modifications or different operating alternatives of the utilities and energy systems. Auditing and accounting of steam and boiler feed water allows reducing steam waste and identifying imbalances. Finally, continuous monitoring allows preventing plant upsets and helps to quickly identify steam wastes.

It is important to emphasize the high involvement and motivation of plant operators found since the very beginning of the implementation projects. Coordination among plant areas in order to implement the proposed optimization recommendations is also a critical issue, so management involvement is crucial. The robustness of the tool helped operators to gain confidence on the overall optimization system.

Finally, users' acceptance and widespread use, for both engineers and operators, is one of the key issues for a successful implementation.

The authors, working tightly with clients and Soteica engineering team members, have participated in many other successful implementations which resulted in significant economic benefits for the refining and petrochemical industries involved. 

References

1. "Auditing and control of energy costs in a large refinery by using an on line tool", D. Ruiz, C. Ruiz, J. Mamprin, Depto. de Energías y Efluentes Petronor, (2005), ERTC Asset Maximisation Conference organized by Global Technology Forum, May 23-25, 2005, Budapest.

2. "Reducing refinery energy costs", Ruiz D., Ruiz C., Nelson D., Roseme G., Lázaro M. and Sartaguda M., (2006), Petroleum Technology Quarterly, Q1 2006, pages 103-105.

3. "Online energy management", S. Benedicto, B. Garrote, D. Ruiz, J. Mamprin and C. Ruiz, Petroleum Technology Quarterly Q1 2007, pages 131-138.

4. "The Use of an On-line model for Energy Site-Wide Costs Minimisation", García Casas, J.M., Kihn, M., Ruiz D. and Ruiz C., (2007), ERTC Asset Maximisation Conference organized by Global Technology Forum, May 21-23, 2007, Rome.

5. "Energy System Real Time Optimization", D. Uztürk, H. D. Franklin, J. M. Righi, A. T. Georgiou, NPRA Plant Automation and Decision Support Conference, Phoenix, USA, 2006.

6. "Site-wide Energy Costs Reduction at TOTAL Feyzin Refinery", Département Procédés - Energie, Logistique, Utilités, TOTAL - Raffinerie de Feyzin, D. Ruiz, J. Mamprin and C. Ruiz, (2007), ERTC 12th Annual Meeting organized by Global Technology Forum, 19-21 November, Barcelona.

7. "Online Energy Management", D. Ruiz, C. Ruiz, D. Nelson, Hydrocarbon Engineering, September 2007, pages 60-68.

8. "A Watchdog System for Energy Efficiency and CO2 Emissions Reduction", Diego Ruiz, Carlos A. Ruiz, European Refining Technology Conference (ERTC) Sustainable Refining, Brussels, Belgium, April 2008.

9. "Online energy costs optimizer at petrochemical plant", Marcos Kihn, Diego Ruiz, Carlos Ruiz, Antonio García Nogales, Hydrocarbon Engineering, May 2008, Pages 119-123.

10. "Finding Benefits by Modeling and Optimizing Steam and Power Systems", M. Reid, C. Harper, C. Hayes, Industrial Energy Technology Conference (IETC), New Orleans, May 2008.