Reducing Costs with a Model Predictive Control
In today's marketplace of spiking energy and fuel costs, demand for alternative fuels is at an all-time high. With the recent energy bill doubling production of ethanol by 2007 and consumer initiatives demanding more environmentally friendly fuel sources, ethanol producers are faced with unprecedented demand.
In today's marketplace of spiking energy and fuel costs, demand for alternative fuels is at an all-time high. With the recent energy bill doubling production of ethanol by 2007 and consumer initiatives demanding more environmentally friendly fuel sources, ethanol producers are faced with unprecedented demand. In fact, demand for corn-based ethanol is growing faster than the supply of the corn by-product itself. Ethanol producers are looking for solutions beyond expensive capital investment that will allow plants to maximize production and deliver high yields, while making more efficient use of energy internally. Given energy's role as the second highest cost of plant operation and the drive to practice what they preach, energy efficiency is key to profitability for ethanol manufacturers.
Focusing on plant operations is the most rapid way to achieve consistently substantial energy reductions, but this is balanced against the focus on production, yield, quality and staffing decisions. Model predictive control (MPC) provides a comprehensive software solution that allows manufacturers to achieve all of these objectives simultaneously and efficiently.
MPC provides a consistent, model-based intelligence layer to assist minute-to-minute operator decisions that affect overall plant and production performance. In contrast to capital changes designed to improve the energy equation, such as installing a supplemental regenerative thermal oxidizer (RTO) or an additional centrifuge, MPC solutions reside on top of existing systems. They can be installed in a relatively short timeframe, and—most importantly—without a plant shutdown.
In the last six months, a number of ethanol manufacturers have installed MPC solutions to reduce energy, increase production, and enhance yields. Most have reported a rapid payback and solid return on investment with the following results:
Reduction in energy consumption of 1 to 5%;
Increase in production of 1 to 6%; and,
Payback within 3 to 12 months.
What is model predictive control?
Since the factors that affect plant profitability are dynamic—raw material quality, energy prices, types of energy used, age of equipment, market demand—a viable control solution must also be dynamic. MPC enables continuous optimization of the dynamic production process to achieve a number of key production objectives simultaneously: efficiency, quality, safety, and throughput. An MPC solution allows management and production teams to identify the best operating parameters while targeting and controlling the process to achieve these goals.
An adaptive MPC solution can sense changes in the production environment and automatically adjust to those changes to remain at optimum efficiency. Finally, when objectives change (as when energy prices soar), rescheduling production can reduce energy costs. The objectives of the MPC solution can be changed without altering fundamental hardware or process models.
A key differentiator for MPC is the ability to calculate in real time and optimize a continuous mathematical model. MPC is an inherently multivariable solution, designed to handle non-trivial challenges described above. MPC takes into account multiple influences simultaneously, and enables more rapid response to process changes coordinated across many operator handles available on dryers, evaporators, distillation columns, cook, fermentation or molecular sieves.
MPC is based on different technologies than rule-based 'expert' control or fuzzy logic systems. Internal mathematical process models are developed based on equipment performance of a dryer or distillation train, including quality, energy, emissions and production results. The MPC controller uses this internal model to predict performance against specified optimization criteria (such as quality limits and energy costs) and optimizes various regulatory controller moves that provide the best result from the model — thus creating the best results across the entire process.
Ethanol plant examples
To remain competitive, a prominent ethanol producer in Iowa selected an MPC solution in 2004 to increase production while lowering energy costs. This company saw the need for a solution that would allow the plant to respond to rising ethanol demand while maximizing efficiency. The producer deployed MPC on its dryer operations. In just four months, the plant reduced energy use and realized rapid payback. Specific improvements included:
Increase in ethanol production of 6%;
Increase in dried distillers grains (DDGS) throughput by 7% ;
Reduction in overall plant energy utilizations with a 14.5% natural gas BTU reduction per gallon of 200 proof ethanol;
Enhanced quality of DDG increasing the addressable market; and,
More than an estimated $2.4 million in annual value.
Another ethanol company, Glacial Lakes Energy, also located in Iowa, deployed MPC in its plant to allow production process optimization of its molecular sieve/distillation area. After six months, they saw no loss in ethanol yield while enjoying:
Reduction in energy use per gallon by 1 to 2%; and,
Increase in plant capacity between 3 and 10%.
Since then Glacial Lakes has further reduced energy costs by applying MPC to the dryers.
So why is MPC such a viable option for ethanol producers? Many plants have invested in automation and equipment to improve the efficiency of their operations. Because of this, ethanol plants today are more efficient than their predecessors. There is, however, significant potential to maximize efficiency and productivity of existing assets.
MPC taps into real-time production and analyzer data, enabling plants to identify ways to achieve multiple objectives continuously and efficiently push the constraints of their operating environment, while simultaneously driving energy consumption down. And with rising energy costs, this is a key objective for ethanol producers to meet rising consumer demand better and increase their overall plant profitability.
Ric Snyder is senior product manager at Pavilion Technologies, maker of model-based software for a variety of industries. A version of this article previously appeared in Ethanol Producer Magazine.
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