Energy is a big cost component of ethanol processing, varying between 10 percent and 20 percent of direct costs. The variety of specific energy systems is nearly as varied as the number of plants. Most differences are related to the initial plant design and, once built, modifications are fairly limited. The typical dry-mill operation uses 30,000 to 40,000 Btus per gallon to process corn to ethanol and distillers grains.1 The sources of energy include both electrical and thermal energy. Dryers and evaporating systems account for about 53.5 percent of thermal energy utilization and 41 percent of electrical energy utilization. Distillation and dehydration systems account for approximately 32.1 percent of thermal energy, but only 2 percent of electrical energy. 
 
 Multiple Challenges, One Solution 
 
 Plant operational focus is the most rapid way to consistently achieve good energy numbers, but this is, of course, balanced against focus on production, yield, quality and staffing decisions. Model predictive control (MPC) provides a software solution that allows manufacturers to tackle all of these objectives at once. MPC provides a consistent, model-based intelligence layer to assist the minute-to-minute operator in decisions that affect overall performance. In comparison to capital changes—such as installing a supplemental regenerative thermal oxidizer (RTO) or an additional centrifuge, which would also improve the energy equation—MPC solutions sit on top of existing systems and can be installed in a relatively short timeframe without a plant shutdown. 
 
 In the past six months, a number of ethanol plants have installed MPC solutions to reduce energy, increase production and enhance yields. Most have reported a rapid payback and solid return on investment.
 

 How MPC Solutions Work
 
 Since the factors that affect plant profitability are dynamic—raw material quality, energy prices, types of energy, age of equipment, market demand, etc.—a better control solution is also dynamic. MPC solutions enable continuous and dynamic optimization of the production process to achieve a number of key production objectives simultaneously and in an efficient, high-quality, safe manner. An MPC solution enables ethanol plant management and production teams to identify the best operating parameters and achieve those goals. In addition, an adaptive MPC solution automatically adjusts to changes in the production environment. Finally, when objectives change, as when energy prices soar, rescheduling production can reduce the impact of energy costs. The objectives of the MPC solution can be changed without altering the fundamental hardware or process models. 
 
 Many plants have invested in automation and equipment to improve the efficiency of their operations. Still, there is significant untapped potential available with increased automation. MPC taps into the real-time production and analyzer data, enabling plants to identify ways to achieve multiple objectives at once and push the constraints of their operating environment. 
 
 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 the non-trivial challenges described above. MPC takes into account multiple influences simultaneously and enables a more rapid response to process changes coordinated across the many operator handles available on dryers, evaporators, distillation columns cook systems, fermentation or molecular sieves. 
 With MPC solutions, 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 and optimizes the various regulatory controller moves that provide the best result from the model.
 
 Implementing MPC
 
 MPC is implemented by defining model inputs and outputs, and developing a control matrix where the relationships are mapped. The inputs and outputs vary depending on the unit objectives. A good MPC solution begins with the goals first. These goals are principally referred to as model outputs or controlled variables (CV). In distillation or dryer examples, the CV's could be base losses or DDGS moisture. Economic objectives can also be CV's, such as maximum production or minimized energy use at a production target. Operating limits are also imposed, and these are known as constrained control variables (CCV). Examples of CCVs are pump limits, maximum hot dryer and minimum thermal oxidizer temperature. There are two types of model inputs: those the controller can manipulate and those that are uncontrolled influences on the process. Manipulated variables (MV) are those that the regulatory control system controls like dryer gas flows, wet cake syrup flow or rectifier column reflux. Disturbance variables (DV) that are not adjusted by a control application could include ambient or beer column feed temperature.
 
 The controller corrects and makes moves based on the difference between actual and predicted behavior. The process model incorporates the process knowledge available from process data historians, process engineering know-how and equipment design data. This process model in addition is a dynamic model that estimates not only how much any change will make on the energy and quality, but also how fast changes will occur. MPC dynamic models have been ideally utilized to avoid swinging control moves on slower processes, such as a dryer, where corrections are made two or three times before the result of the first correction is seen in the product. A dynamic mathematical model understands and compares results against the anticipated and historic dynamic results.
 

 DDGS Drying Energy Reduction 
 
 An MPC solution can reduce energy costs per gallon with distillers grains drying in many ways, including capacity increase, avoiding overdrying avoidance, excessive thermal oxidizer (TO) temperatures and water removal balance between evaporation and drying frequently in multiple dryers. Capacity increases are generally achieved with only moderate total energy requirements so that Btus per gallon go down with increased capacity. By continuously pushing capacity against plant bottlenecks, MPC projects frequently increase capacity sufficiently so that energy numbers are also measurably reduced.
 
 Another way to reduce energy cost through MPC is tighter moisture control. Overdrying can be reduced, creating savings on energy that directly affects the bottom line. Projects typically increase DDGS moisture by over 1 percent, and this results in both energy savings and increased DDGS yield per bushel. Along with these benefits, many customers with dryer controls have increased customer satisfaction from their improved feed consistency. 
 
 Most plants demonstrate functioning thermal destruction effectiveness by maintaining their thermal oxidizer hot box temperature above a limit demonstrated to sufficiently destroy fugitive VOC and carbon monoxide emissions. But because of the challenges of providing a stable process steam pressure and maintaining a negative firebox pressure few plants can continuously manage to drive TO temperatures to their minimum allowable limits.
 
 Finally, to dry all stillage within a specified product moisture target, evaporator solids and Dryer a moisture as well as evaporator syrup addition distribution can be considered independent degrees of freedom to optimize plant wide drying at maximum efficiency. There is generally an optimum balance between drying in dryer A and B that changes from plant to plant depending on tons/day dried the current performance and the equipment limits on each dryer.
 
 Energy Reduction without Drying
 
 Plants with or without limited drying operations still have several opportunities to reduce energy costs in wet cake product moisture management. A model-based control system would control evaporator solids and also wet cake solids based on online inferred property models. Using these models on direct quality control, the evaporator syrup solids can be increased or decreased depending on the balance between wet cake moisture produced in the centrifuges and the amount of syrup being produced. 
 
 In one example, a 50 MMgy ethanol plant invested in MPC on its dryers and evaporators to improve production and product quality. While energy cost reduction was not a primary goal, overall cost reduction was a secondary goal for the plant's control solution. The client and project team pursued a strategy to maximize the full value of the application. As a result, the client achieved greater than normal benefits the plant.

 
 The MPC solution continually calculates and implements the best control solution at any given condition and time to accomplish the desired targets. The plant's installed the MPC solution to maximize dryer feed subject to the dryer and centrifuge constraints, while maintaining the desired moisture targets and balancing syrup dryer application with excess production. Because dryer limits, modified wet cake capacity targets and/or energy values can change, the optimal solution is continuously calculated and the controller implements the new set points through the regulatory controllers instantly. 
 
 The Plant's first mill MPC installation exceeded all of the criteria set for a successful application. The plant reported a greater than 10 percent capacity increase and savings Btu per gallon produced. Since implementation, the plant has realized the following benefits from the implementation of MPC:
 <ul>
 <li>Increased production rate by greater than 10 percent </li>
 <li>Reduced specific energy consumption by greater than 14 percent </li>
 <li>Increased product consistency measurably by the plant and its customers </li>
 <li>Increased DDGS yields</li>
 </ul>
 
 Pavilion's MPC solution allowed the plant to reduce operating costs, increase production and improve product quality. The plant increased its overall efficiency and reduced energy costs. The facility is now in the process of implementing MPC on its distillation, sieves, slurry solids and water balance, already achieving measurable results. Its experience is that these systems are robust, reliable and have a high operator utilization and acceptance.	
 

 As with dryers, there are many ways to reduce energy use per gallon by implementing an MPC solution on the distillation and sieve units producing anhydrous ethanol product. MPC applications on cook and fermentation can also have a significant positive influence on a plants energy balance. 
 
 Model predictive control solutions can be used to achieve multiple objectives simultaneously—reduced energy use, increased production, emissions compliance, or improved product quality and yields. Additionally, if one objective becomes more important, the objectives of the control solution can be changed focusing on that goal. 
 
 Michael Tay is a technical account manager with Pavilion Technologies. Maina Macharia is a chemical engineer involved in the management and development of model predictive control solutions with Pavilion. For more information about the topic of this article, contact Amy George at Pavilion Technologies at <a href="mailto:ageorge@pav.com">ageorge@pav.com</a> or (512) 438-1443.
 
 1 The Alcohol textbook' 4th Edition K.A. Jacquest, PHD, T.P Lyones, D.R. Kelsall Nottingham University Press, Nottingham, ISBN, 1-897676-13-1

Reducing Energy Costs with Model Predictive Control Solutions

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Reducing Energy Costs with Model Predictive Control Solutions