Take The Guesswork Out Of Utility Management

Figure 1. Flow diagram highlights the complexity
of Eastman Chemical’s Kingsport powerhouse.
(Click on illustration for larger image).

The ProblemWith so many choices to be made, operators had an extremely difficult time managing the system economically. During a typical shift, they coordinated steam generation between four powerhouses operating with a wide range of equipment. Loads varied continually (Figure 2). Equipment operates in different ranges of efficiency. Balancing the amount of power produced internally with purchased power added another level of complexity. Managing the regulatory limits on NO_x, a significant challenge during the warm months, posed a headache for the operators. The system was so convoluted that it was difficult for anyone to determine the best solution to the power puzzle at any given time. It was a Rube Goldberg system. The accepted practice was to make all the power we could because equipment runs most efficiently at 100% capacity. This wasn’t always the most economical solution given the dynamics of the system. The operators had guidelines on how to run their equipment but these were based on outdated calculations made using fixed fuel prices. The SolutionWe knew that achieving utilities savings required a powerful new data analysis tool and a thorough understanding of the steam and power generating system. This effort required assembling a team of plant engineers and control specialist to develop a working model of the system. The complexity of the process led us away from a control system to an advisory system. Our first decision was the selection of the platform. After a period of searching, we chose Emerson’s AMS Suite: Real-Time Optimizer. Our decision was based on its track record of delivering significant benefits with complex systems. This real-time, online optimization system is capable of feeding decision-makers a continuous stream of economic information calculated from process variables. Once the optimization system was in place, we felt that it challenged many of our long-held beliefs — leading us to previously unforeseen process improvement opportunities.

Figure 2. Screenshot of optimum cost and baseline
cost of powerhouse shows the positive impact of the
software. (Click on illustration for larger image).

Our first step was to create a reliable process model of the utilities infrastructure and electrical distribution network. This model encompasses the full range of possible operating modes within the power plant. All equipment is included in the model covering each item’s entire operating range. Online monitoring is configured to recognize performance degradation. Live process data are key inputs to the real-time optimizer, which receives updates every few minutes. The AMS Optimizer compares plant operation costs and automatically generates optimum loading instructions. Operators can then adjust performance parameters for each boiler and power-generating unit to maximize efficiency. The optimizer also monitors overall efficiency. For example, the optimizer may recommend purchasing power as a means of reducing overall energy costs. If changing conditions make in-plant generation of electricity more cost-effective, our decision-makers are immediately informed of the opportunity. Wise “make or buy” decisions are the biggest drivers behind our documented savings, and we frequently purchase power when the optimization system reveals the economies of balancing out what we don’t produce internally. One powerhouse is control central where the operators monitor the easy-to-use optimizer interface, a Graphical User Interface (GUI). The GUI displays an up-to-date summary of costs and operating conditions. Operators in the other powerhouses also have access to the GUI, which provides instructions to control their boilers for the production of power. Our goal is to minimize costs while meeting the demand for power and steam from throughout the plant. During the ozone season, from May through September, the AMS optimizer predicts NO_x emission rates. This estimate is based on fuels being burned to ensure that emission limits aren’t violated. The documented NO_x savings can even be traded at the end of the NO_x season. (The EPA’s Clean Air Act Amendments of 1990 set a goal of reducing NO_x by 2-million tons from 1980 levels; one of the provisions of that act establishes NO_xtrading.) For the optimizer to operate effectively, instrumentation throughout the power generation and steam distribution systems must be highly reliable. The optimizer monitors instruments. When suspect measurements are received, the data validation capabilities within the optimizer are configured to reject suspect readings and substitute the last good readings or default to expected values. The system also alerts operators of an instrument fault. Accurate, well-calibrated instrumentation is a key to success in controlling any process, especially steam and power generation. For example, if the measurement of produced electricity was in error by only +2 MW, imported electricity would be 2-MW higher than the optimum. The error in projected costs, the added cost, could be more than $350,000 per year. This is a powerful incentive for maintaining a vigilant instrumentation PM program. Industry Recognition
Earlier this year, Eastman received the American Chemistry Council’s Exceptional Merit award for “significant improvements in manufacturing at a plant site” based on the utilities optimization system. This recognition from our peers in the chemical industry acknowledges our efforts to conserve energy. In turn, we appreciate Emerson’s contribution to this award. Their technology and expertise were instrumental in helping us meet our energy cost reduction goal. Emerson provided a low-risk solution that has generated high returns. An audit was conducted comparing past operation against new operation with the optimizer. The architecture for the optimizer is presented in Figure 3.

Figure 3. The chart above shows the approach
used by the chart optimizer, which was responsible
for 90% of the savings achieved.
(Click on illustration for larger image).

This audit verified the savings estimated when the project began — a payback of less than six months! To help define the true benefit of the new system, savings were broken down further. The online optimizer was responsible for 90% of the savings; a further 10% was accrued from the offline systems. These systems monitored less critical components. Periodically, stored data are downloaded from these independent monitors. With AMS Optimizer, we have a robust and reliable system allowing us to accurately sustain economic decisions that will continue to generate savings for years to come. Lessons learned from operating the optimization system may help to improve the process and lead to further improvements in controls and real-time cost savings. C. Lemuel Mixon is a technical associate in Utilities Distribution services with Eastman Chemical Co., Kingsport, Tenn.; e-mail him at clmixon@ eastman.com.