Saving Energy with Advanced Automation

by Jim Cahill

Emerson's Doug White sent me his presentations from the recent AIChE spring meeting. Doug is a principal consultant and vice president for advanced process control (APC) services, and has many years of experience justifying, designing, installing and commissioning APC applications for process manufacturers.

Given rapid rising energy costs, his presentation, How to Save Energy through Advanced Automation, caught my attention. He starts by showing an upward trend in natural gas prices (in one word—ouch!) Doug makes the point that process energy usage is often the largest controllable cost in most plants.

Doug shows energy flows for process manufacturers in different industries including chemicals, pulp and paper and oil refining. He also gives some typical percentages of the energy flow inputs and outputs. For example, a typical refinery's sources of energy include 1% purchased steam, 25% purchased fuel, 64% raw materials consumed as fuel and 10% purchased power. This energy is used in steam production and central power production in the power plant. In the process and offsites areas, the energy is mainly consumed in the process-fired equipment, direct fuel usage and electric motor drives. Energy not consumed in the process is exported as steam, fuel and power.

Applying better automation and APC can help improve efficiencies around individual equipment like boilers, heaters and kilns (links are to earlier posts where equipment efficiency stories have been chronicled.) Savings can also be achieved at a unit, multi-unit and site level by finding opportunities in optimization, waste heat recovery, and off-spec/waste minimization.

As the earlier percentages indicate, you may have a control loop heavily involved in your plant's energy usage. It may well be worth improving the measurement, control valve performance and loop control performance to reduce variability and energy consumption. Also, your process may have bypasses around production equipment that may be compensating for poor control through the equipment. Optimized control can eliminate the need for these bypasses.

The presentation is loaded with specific examples including stem systems, combustion control, heaters, distillation controls, plant utility systems, facility optimizers, boiler load allocation and site energy balances. Some examples like power boilers include return on investment (ROI) calculations that may assist you in your project justification efforts.

I wanted to highlight some key points Doug makes around heater optimization. If there is resistance in improving heater controls because the damper control is are not reliable, then he recommends adding positioners to the dampers. Bring the feedback to the control system and then analyze and fix any controller problems. If the next objection is on-line analyzers don't exist or are not maintainable, Doug notes that analyzers are cheaper and more reliable, especially mass flow meters. With today's higher fuel costs, these analyzers should be well justified.

There are likely many areas to look for energy savings. Doug recommends a disciplined approach to evaluation and analysis to prioritize the opportunities. Given the increasing costs of energy and the fact that this is often the largest controllable cost in a process manufacturing plant, it may make sense to establish a program around saving energy and apply focused efforts in prioritized projects to reduce overall energy consumption.

Technorati Tags: | | | | | | | | |

May 13, 2008 in Boilers, in Distillation Column, in Energy Management, in Fired Heater, in Lime Kiln, in Process Optimization | Comments (0)

Lime Kiln Model Predictive Control

by Jim Cahill

Moore’s Law foretold of computer processing power doubling every eighteen months when the idea was introduced by Intel’s cofounder Gordon Moore in 1965. This law has application in process automation since the microchips that power today’s controllers and I/O have taken advantage of this increasing power.

Advanced process control algorithms, once solely running in the domain of host-level computers running above the automation systems, are now available down in automation system controllers. These algorithms include model predictive control, fuzzy logic, and neural networks to name a few. The benefit is that these controllers are closer to the action of the running process and can use the diagnostic information in smart field devices to make sure they know when to control, and when to leave control in manual with the operators. These controllers are also available in redundant configurations, something that was more difficult and expensive to achieve with host computers.

Another result of this ever increasing processing power is that more applications can take advantage of these advanced process control algorithms. What was once strictly the domain of large applications like refinery optimization due to the cost, complexity and expertise required, can now be applied to smaller applications.

Lime Kiln ProcessLime kilns found in pulp and paper mills, cement and steel mills are a great example of a smaller application that is well suited for model predictive control (MPC) technology. I spoke with Gordon Lawther, a consultant in our Pulp and Paper industry center. Gordon explained that lime kilns are highly interactive in that a change to one process variable impacts the others. They are also constrained by excess oxygen, hood draft pressure, and the kiln stack emissions.

Using model predictive control allows the lime kiln to be operated as a unit instead of a collection of loops which all interact with one another. Since it’s an empirical model of the running processes it can predict into the future to help operators see where key variables are heading and help them resist manually intervening and inducing variability into the process.

Gordon noted that this increased variability is reflected in the lime quality and fuel usage which increases operating costs. The team has consistently documented annual energy savings of 10% or more and maximized mud throughput has saved more than $500,000 USD per year in purchased lime.

The team has packaged their expertise into a SmartProcess Lime application. It uses Emerson model predictive control technology and the expertise Gordon and team bring in benchmarking the existing process, creating and commissioning the models, and measuring the performance improvements. The importance of training operations staff cannot be overstated and is also an integral part of all SmartProcess Lime projects.

Technorati Tags: | | | | | |

August 3, 2006 in Lime Kiln, in Process Optimization | Comments (0) | Trackback (0)