Emerson Process Experts

James Beall delivered a Back to the Basics – Process Control Diagnostics Improves Refinery Performance presentation at the recent AIChE spring meeting. James, whom you may recall from earlier variability management posts, is a principal process control consultant. He's a senior member of Emerson's variability management consulting team.

In this presentation, James stressed what he normally stresses with process manufacturers—that some of the largest and most frequent opportunities exist in basic process control. These opportunities include eliminating variability at the source, tuning the controllers to meet the control objective, using ratio, cascade and feed forward control as well as using a process analysis system to diagnose problems and tune loops. Addressing these opportunities also builds a control foundation essential for any advanced process control (APC) initiatives.

He referenced a 1997 McKinsey study that showed 50-60% of the value realized from a process optimization project comes from addressing loop variability. The balance 40-50% comes from applying APC on top of these optimized loops. The financial results from reducing variability are being able to operate closer to constraints such as specification limits. Benefits can come from reduced energy consumption, less waste and rework, higher yields, higher quality, etc.

The variability management team keeps statistics on control loops with excessive variability from site audits. The major causes of this variability include control valve performance (30%), improper tuning (30%) and improper process design (20%).

James shared several valve-performance examples including a regenerator pressure valve. By looking at the setpoint, pressure, output, and valve position trends, he spotted the valve sticking and then jumping 3% followed by a quick spike of another 2-3%. This caused periods of oscillations before settling out. Once the sticking problem was addressed, the oscillations became tiny ripples on the trends. Similarly, poorly tuned loops can cause large oscillations impacting overall process variability.

He noted that you must have a process dynamics analysis and diagnostic tool of some type to pinpoint these sources of variability. Problem identification is the first step in corrective action. And these problems may be significantly impacting the overall efficiency of the process.

James described some of the tests that he and the variability management consultants use with the Entech Toolkit. One of the most important tests is to identify the process dynamics so that the control loops can be properly tuned. Emerson's Entech Toolkit can identify common dynamics such as first order, second order overdamped and integrator+lag. Dynamics that are more complex can be identified by this process analysis toolkit (11 tests in total) and the associated controller can be properly tuned. Many of the more complex process dynamic responses cannot be identified by less sophisticated analysis systems.

If you have the bandwidth and inclination to learn the skills to do it yourself, James recommends three Emerson Education Center courses: Process Dynamics, Control and Tuning Fundamentals, Process Analysis and Minimizing Variability and Modern Loop Tuning.

Tags: process control | diagnostics | AIChE | APC | loop optimization | cascade control | feed forward control | Lambda Tuning | refining | refinery |

May 15, 2008 in Process Optimization, in Refining, in Variability Management | Comments (0)

I received an email from a university student with a great question the other day. It prompted a great answer from Pete Sharpe, a Principal Advanced Automation Consultant. You may recall Pete from earlier posts on process optimization.

I've retained the anonymity of the person asking the question by editing the question:

I am doing my thesis on estimation of benefits by implementation of advanced control, I read your articles in this field and it help me so much, but I still have some questions, I would like to know if you could give me information about how to calculate the benefits to pour point, viscosity and Research Octane Number (RON). I will be grateful for your help.

Pete responded:

I was forwarded your request about calculating benefits. I've had some experience in this area. Are you estimating benefits for a blending process? If so, the opportunity is to reduce variability and approach the specifications closer using less of the more valuable components. So instead of making 87.5 RON on the average, you reduce it to 87.1. The value is the total blend rate times the difference in average octane times the octane barrel cost.

Anyhow, I'm attaching a paper that perhaps might help describe how these benefits are calculated.

I contacted the ISA and received permission to re-host this paper, Estimating Benefits from Advanced Control (Copyright © 1986 ISA. Reprinted by permission. All rights reserved.)

In the paper, the authors (Pete, P.L. Latour, and M.C. Delaney) apply statistical methods to estimate savings from dynamic control improvement and steady state optimization. At the end of the article, they run through a distillation column example calculating annual dollar savings by reducing process variability and thus allowing the column to operate more closely at its limits.

Whether you're a student or a project engineer, you might find the calculations in this "oldie but goodie" paper useful in trying to estimate and quantify the benefits for your project.

Tags: research octane number | benefits estimation | process optimization | advanced control | advanced process control | APC | steady state optimization | distillation column |

May 9, 2008 in Distillation Column, in Process Optimization, in Refining | Comments (0)

Last week I did a post about pipeline surge pressure relief and a technical guide about this written by Emerson's Daniel business. They are known for gas and liquid fiscal flow measurement solutions for the oil and gas industry.

I received a nice follow up note from Dave Seiler about a Latin American refiner who was fighting turbine meter maintenance problems due to large concentrations of foreign materials in the pipeline liquid flow. The problem was so acute that they actually had to install two meters in parallel so they could switch between meters while the other was being maintained.

The refinery engineers worked with the local Daniel team to replace the turbine meters with a 6-inch liquid ultrasonic flow meter. These do not have moving parts, unlike the turbine meters, which were being impacted by the particulates in the flow.

I didn't know much about the ultrasonic technology in flow applications, so I googled around and found a Hydrocarbon Processing magazine article reprint, Use liquid ultrasonic meters for custody transfer, in the Daniel area of the EmersonProcess.com website.

Dave is a co-author of this paper. The article does a great job of simplifying how the ultrasonic technology works. It also includes the math on how the ultrasonic flow measurement works.

My analogy, fresh from a rafting trip down the Guadalupe River, is to imagine that you're floating down the river with an ultrasonic transducer on one bank, and another on the other bank a little further downstream. Ultrasonic pulses are sent between the two transducers in each direction. The pulse traveling across the river from the upstream one to the downstream one will obviously travel faster since it's going across the river with the current. And of course, the reverse is true; it takes longer to travel across the river going upstream against the current. With the formulas in the article and enough perseverance, you can calculate the river's flow rate from these time differences. For the 3D world of pipe flow, the authors' explain:

The resulting time difference is proportional to the fluid velocity passing through the meter spool. Single and multiple acoustic paths can be used to measure fluid velocity. Multipath meters tend to be more accurate since they collect velocity information at several points in the flow profile.

Now back to the story… after the installation of an ultrasonic flow meter, the refiners saw that the meter was reporting low flow rates when the product in the pipe switched between gasoline and diesel.

The local Daniel service technicians collected maintenance logs using their Customer Ultrasonic Interface software (CUI) and sent it to the support team in Houston for detailed analysis. The team verified that the meter was working correctly for both liquids. They deduced that the flow was being diverted somehow during the transmix, or product switchover, where both products are flowing through the pipe until the switchover has been completed. This was possible because of the meters ability to accurately measure both flow rate and speed of sound of the liquid passing through the meter with extremely high accuracy.

The refiner verified that this is what indeed was happening where this transmix was being routed away through a smaller pipeline for further reprocessing. With the age of the refinery and the retirement of experienced operators, the current operators had not been able to see this transmix operation occurring in their process. The refinery engineers were impressed that the team in Houston could deduce this from their analysis of the data.

The refinery engineers involved in this project are presenting a workshop at this year's Emerson Exchange in late September. If you face similar challenges, you might want to catch this one.

Tags: flow measurement | ultrasonic flow meter | pipeline liquid flow | turbine meter | transmix | Emerson Exchange |

April 11, 2008 in Custody Transfer, in Emerson Exchange, in Measurement, in Pipeline, in Refining | Comments (0)

I've written about terminal and offsites operations a few times in the past. I had a chance to catch a presentation given by Emerson's Shoyeb Hasanali, who leads the terminal management solutions team.

Shoyeb began by giving us a good grounding on terminal operations. These facilities provide receiving, shipping and storage facilities for liquid or gaseous products processed by or produced in a refinery or petrochemical complex. These sites typically include tank farms, blenders and loading and unloading facilities. The loading and unloading facilities may handle truck, rail, marine or pipeline transport of these liquid and gaseous products.

Some of the issues terminal operators currently face is a lack of spare capacity to handle additional bulk products, increasing safety, environmental and regulatory compliance requirements, and an increasing number of product variations.

The rapid price increase in refined products has caused a shift in the movement patterns and logistics in the transportation of these products. The automation and information systems within existing terminals were not designed for the current economic climate and rapid changes in spot prices. Terminal operators often have disparate automation systems for custody transfer, loading/unloading, blending, vapor recovery and other units.

Shoyeb and his team of terminal management solution consultants work with terminal operators to provide front end engineering design (FEED) to identify the opportunities to improve the flow of accurate and timely information required in rapidly-changing price world.

The FEED study is typically followed by functional designs, functional requirements and factory acceptance testing for the hardware and software used in the solution. Much of the technologies for these solutions come from various businesses within Emerson Process Management. These include Saab Rosemount tank gauging, Daniel custody transfer, metering skids, loading rack presets, Micro Motion flow and density measurement, METCO metering services and DeltaV blend control.

The team has delivered projects all over the world on products including gasoline, diesel, jet fuel, asphalt, fuel oil, lube oils, chemicals, fertilizers, liquefied petroleum gas, liquefied natural gas and specialty chemical products.

Tags: terminal operations | offsites | terminal management | tank farms | vapor recovery | tank gauging | custody transfer | blend control |

April 8, 2008 in Blending, in Chemical, in Refining, in Terminal | Comments (0)

Those of us with teenage kids, or memories of their kids as teenagers, or even what we were like as teenagers may recall the question, "Why do I have to learn ____ if I'll never have to use it?" This is very fresh in my mind because I had that very conversation the other night. The blank in this case was chemistry. My point was that you really have no way of knowing what you'll need to know so you might as well learn it.

Well today, I'm reading an article from the August edition of Hydrocarbon Processing magazine written by Air Product and Chemical's Win Hoglen and Emerson's Julie Valentine, a member of the Micro Motion business. The article, Coriolis flowmeters improve hydrogen production describes how accurate steam-to-carbon ratio control improves efficiency in a reforming hydrogen plant located within a refinery. The article explores the chemistry in the reforming process converting the light hydrocarbons (methane, ethane, propane, butane) and water (in a superheated steam state) into hydrogen and carbon monoxide. A shift reaction then converts the carbon monoxide and water into carbon dioxide and hydrogen.

For those not in the refining industry, this hydrogen is needed to scrub the sulfur out of gasoline and diesel to meet the clean fuels regulations that countries around the globe have adopted. The sulfur reacts with the hydrogen to make hydrogen sulfide and then it is further processed into elemental sulfur.

The thrust of the article is not the chemistry lesson I just described, but the challenges to most efficiently produce this hydrogen. A major challenge is the chemical composition of the natural gas since:

…the amount of steam required for the reforming reaction can vary widely depending upon the number of carbon atoms per molecule of the gas (i.e., one molecule of steam is required for each carbon atom, but there can be from one to four atoms).

Traditionally, volumetric flow measurements were used which usually involved differential pressure measurement and gas chromatograph or mass spectrometer analysis. Calculations determine the actual mass flow (carbon mole flow.) Errors in the carbon mole flow result from errors in the volumetric flow when the composition changes. Also, this analytical equipment requires regular maintenance and steam flow must be increased to handle any spikes in carbon mole flow during this maintenance period.

There are problems with both too much and too little steam flow. Too little reduces catalyst life, and production instability that may lead to a costly plant shutdown. Too much steam wastes energy and may require additional capital investments for more steam capacity. The measurement and control challenge is maintaining a constant steam-to-carbon ratio.

Coriolis flowmeters, through the Coriolis effect, measure actual mass flow very accurately and require less maintenance. The drawback is that the mass flow measurement cannot distinguish impurities like nitrogen and carbon dioxide in the natural gas supply.

The article describes testing done where methane concentration ranged between 78% and 89% and ethane between 7% and 15%. Maximum variation in the steam-to-carbon ratio was 0.02 units of steam, much better than the 0.2 in the traditional measurement method. The percentages of nitrogen and carbon dioxide were relatively stable.

From the testing done at various Air Products and Chemical facilities, Micro Motion Coriolis flowmeters are well suited for a natural gas stream that has relatively fixed percentages of inert gases or nitrogen concentrations that do not vary outside of 3% less than design.

A final note, I forwarded this article on to my teenagers to demonstrate the point that one never knows when one might need to know something learned in one's past.

Tags: reforming hydrogen | clean fuels | coriolis flowmeter | carbon mole flow | differential pressure | gas chromatograph | mass spectrometer |

October 8, 2007 in Hydrogen Reforming, in Measurement, in Refining | Comments (0)

I'm catching the session, Realized Benefits from Foundation Fieldbus at the ISA Expo 2007. ARC Advisory Group's Larry O'Brien moderated the session.

The first part of the session looked at BP's use of Foundation Fieldbus (FF) in an onshore oil & gas production SCADA application in the U.S. state of Wyoming. They tested various suppliers systems and field devices in the harsh Canadian winter environment to compare the robustness of the various FF offerings. At each well site of eight wells, control was run in the FF devices, which in turn were connected, into their remote terminal units (RTUs). They saw immediate project savings in the wiring/installation labor/physical footprint savings. A key benefit they saw was remote diagnostics into these devices from a central location.

Next Suncor Firebag shared their Foundation fieldbus experiences. Suncor has standardized on Foundation fieldbus for all capital projects. Firebag has 9-10 billion barrels of reserves using today's extraction technologies. The extraction efficiency is expected to increase over time and these known reserves will increase. Suncor documented commissioning savings of Foundation fieldbus versus conventional devices of one sixth of the time. The benefits cited included faster time to first oil, and instrumentation maintenance practices moved from preventive to predictive. This helps eliminate unnecessary maintenance and avoid unplanned shutdowns.

They are currently looking at applying the statistical processing from the high-speed history in the FF devices to detect flame flutter on furnace and boiler flames. Another application they are investigating again with this high-speed sampled data is early detection of water hammer conditions in piping. This condition can cause millions of dollars in damage to the pipes if it is unchecked.

Emerson's Marcos Peluso gave a quick overview of the case for control in the field and robustness as measured by mean-time-between-failure. The key is the communications path is shorter when the control loop executes between a sensor and final control element than when it is between a sensor, automation system controller, and final control element. The loop can also execute with more certainty with the execution times of the fieldbus segment. Marcos indicated that the application should determine whether control is run in the automation system controller or in the FF device. There are strengths to each approach.

EnCana next presented how they could get gas compressor stations up and running more quickly with Foundation fieldbus devices than with conventional field devices. For their installations, all control was run in the field devices. This designed proved helpful in avoiding lost production when batteries did not hold their charge from solar panels. The remote terminal units and wireless network transmitters dropped out when the battery voltage dropped, but the FF devices kept running. Operators drove to site after they lost communications but found the compressors continuing to run with the loops fully in control. Overall, they we able to reduce operating expenses by linking together their remote compressor stations by centralizing the operators window into their decentralized control. Troubleshooting could be done remotely which reduced downtime.

Finally, Shell briefly shared their Foundation fieldbus experiences. While they agreed with the project savings others had experienced, they saw the biggest value in moving from preventive to proactive maintenance. They recommended this is where process manufacturers should spend the bulk of their planning efforts. This process often involves a cultural change so it takes time and quite a bit of planning and execution. Their approach is to have focused resources continually reviewing the diagnostics from intelligent field devices and performing maintenance based on this information. They cited savings in the millions of dollars from shifting to this approach.

Tags: Foundation fieldbus | capital projects | preventive maintenance | proactive maintenance |

October 4, 2007 in Foundation Fieldbus, in Oil & Gas, in Refining | Comments (0)

I've highlighted the topic of plant turnarounds (planned downtime for maintenance) a few times in the past. Back from the Emerson Exchange, here's my take on the Smart Turnaround workshop. For continuous processes that run for years, this turnaround provides opportunity to update, fix, repair, and replace a host of plant assets including instruments, valves, electrical distribution equipment, connectors and cabling, and the overall performance of the process.

The Emerson presenters looked at the advanced planning that can be done from these various perspectives. From these diverse areas of expertise, diagnostic testing helps develop a turnaround plan that prioritizes critical asset work, defines the scope of work, develops the schedule for the work, and identifies the parts and people required to best get this difficult work done.

Chris Forland an operations consultant whose work I've highlighted in earlier posts kicked off the session discussing some of the challenges of the turnaround process. A big one is finding problems you didn't expect while in the turnaround. These unexpected problems cause extra charges and delays. Chris discussed ways that Emerson turnaround specialists can help with the detailed planning to make sure the work is efficiently performed during the turnaround. He noted that less time to plan mean less flexibility as the turnaround date approaches. Other challenges included maintaining compliance with safety and regulatory compliance, working with budget constraints, reducing process variability, losing experienced personnel due to infrequency of turnarounds, and pressuring of short turnarounds due to sold out condition of produced product.

Scott Grunwald, a turnaround business manager in the Instrument & Valve Services business, recommended that with the valves and instruments, you start by building the plan based on the benefits to be achieved the roles of all participants in the maintenance activities, and the prioritized list of activities and anticipated timelines. The process starts with a walk down of the facility. Next, FlowScanner is used to measure internal valve conditions to identify problems to address during the turnaround. When it's time for executing the turnaround, only valves needing significant work are removed. Other valves are repaired in place.

The team often brings an on-sight mobile trailer that is a self-contained workshop to rework the instrument and valves right on-site. This helps to expedite the repair process.

Looking at turnarounds from an electrical reliability perspective, Steve Metzger described the goal--to prioritize and focus the resources by pre-diagnosing troubleshooting, followed by the planning of the repair services and parts required to get the lead times properly. The key is to do as much pre-work as possible, fix what's possible, and remove it from the scope of the turnaround to lessen the pile of work to be done.

On-line partial discharge testing before the turnaround detects cables with degrading insulation that could cause short circuits and unexpected downtime. This testing helps determine which cables are OK and which need to be replaced during the turnaround.

James Beall, also highlighted in earlier posts, summed up the goal of a Smart Turnaround--to identify the items you can fix in advance, and prioritize what can't be in the turnaround plan. James and the variability management consultants look at the control performance and opportunities to reduce process variability through better tuning. James gave an example of a mixing temperature control loop where the deadtime was nine minutes between a change in setpoint and response the temperature was changing. The problem was not in the loop tuning but rather in the lag caused by the temperature transmitter being located 250 feet from where it should have been. Finding this early in the process allowed this installation mistake to be scheduled and fixed during the turnaround.

Chris closed this presentation with how you can look at the return on investment to help justify the experts required to make the planning and execution of the turnaround a success. It's a bit of a chicken and egg scenario since you don't know what type of ROI this turnaround planning can create without having the experts come in to begin the process of identifying improvement opportunities.

Chris has developed a model based on turnaround experience with typical costs from each of the aspects of turnaround planning and typical costs for the maintenance activities. This model is in an excel spreadsheets so that the assumptions can be easily changed to fit the unique aspects of each process manufacturer. Both cost avoidance and increased revenue from improved plant performance is calculated, each based on the size of the process and amount of equipment considered.

By taking a comprehensive planning approach, and getting an early start, turnarounds do not have to cause quite the number of gray hairs that they have traditionally been known to cause.

Update: Mitzi Amon, director of marketing for Emerson Electrical Reliability Services team adds that the prioritization is accomplished by performing online diagnostic testing prior to the turnaround to determine what electrical equipment needs to be serviced during the turnaround. This helps clearly define maintenance work scope during the turnaround and what can be done prior to the the turnaround.

Tags: turnaround | electrical reliability | partial discharge testing | variability management |

September 20, 2007 in Asset Optimization, in Chemical, in Emerson Exchange, in Plant Equipment, in Refining, in Variability Management | Comments (0)

A colleague pointed me to an article, Timeline of a refinery pump failure and how it could have been prevented, on the Belgium-based EngineeringNet.be website. The story was about a South American refinery that had a high-speed centrifugal pump fail catastrophically resulting in production losses and large repair costs. Todd Reeves is in Emerson's Machinery Health Management team, part of the Asset Optimization organization.

What happened was an inboard bearing lost lubrication, overheated and finally seized up. The unfortunate part of the story is an automated motor-pump train monitor and advanced vibration analysis system had been installed four months earlier and was working properly.

This monitoring equipment included the CSI 9210 Machinery Health Transmitter connected to the automation system via Foundation fieldbus. This equipment did its job communicating advisory alarms it began to detect problems in the lubrication system.

These alerts went unheeded until they became maintenance alerts and ultimately failure alerts. Todd wrote that the health curve of the pump deteriorated rapidly in the final ten minutes before failure.

Why? The equipment did its job and dutifully reported the problem. The issue turned out to be more of overall unit tuning and alarm management issues. These alerts had been lost among other alarms coming in.

Working as a team, the refinery and local asset optimization experts reviewed the overall alarm strategy and identified opportunities to reduce the alarms and alerts coming in to the operators.

Specifically for the pumps, a best practice was established to add additional temperature measurements on the pump. Training was established to clarify how these alerts would be transitioned between the operators and maintenance staff. Clarifying this process is important when working with predictive diagnostics. At the time, it is not yet an actual problem—but like this centrifugal pump example—will fail if not addressed.

Tags: refinery | centrifugal pump | bearing lubrication | machinery health | foundation fieldbus |

July 30, 2007 in Asset Optimization, in Foundation Fieldbus, in Plant Equipment, in Refining | Comments (2)

Last October, I featured one of Emerson's advanced automation service consultants, Lou Heavner, and how he worked with Lukoil to create virtual sensors based on neural networks.

Their efforts were told in more detail in the March 2007 issue of InTech magazine. The article, entitled, Crude gets smart, described the Russian refiner's challenge to keep their refined products within specification. They had been relying on lab samples that came back from the lab to the operators only once or twice a day.

To get feedback on product quality and composition more frequently, Lou and the team used neural network blocks in their DeltaV system's controllers to create property estimators. As the article states:

The goal of a property estimator is to provide an accurate gauge of product quality, especially after lab results have become stale, which is most of the time. Property estimators are not intended to eliminate lab analyses, although the frequency of analyses may lessen once estimators are proven. Even though estimators may not be as accurate as lab analyses, they can be worthwhile calculated variables to help engineering and operations personnel monitor, troubleshoot, or understand and control the process.

The article describes the steps the team took to collect the data to train the neural network models. It offers guidance for those looking to implement property estimators. Some examples of their recommendations include:

• The time stamp should reflect the time of data extraction from the process—not when it was scheduled for sampling, or when the lab technician performed the analysis, or when they reported the lab results.
• Avoid filtering or manipulating the process data. Raw snapshot data usually makes for the best models.
• If the process does not vary much, the model will not be reliable if the process wanders into a range with no collected data… the model will be changed to "Uncertain" and the operator can be alerted.

The team believes they may have one of the world's largest installations in terms of neural network models. Currently operating models include ones measuring boiling points, flash points and viscosity on the pre-flash, atmospheric, and vacuum towers.

If operators at your plant are waiting on lab information to make quality adjustments to the process, you may have a business case for creating property estimators to augment the lab sampling process.

Tags: distillation column | neural network | property estimator | virtual sensor | lab sample |

June 6, 2007 in Distillation Column, in Process Optimization, in Refining | Comments (0)

In an earlier post about fired heater efficiency and reliability, I had spoken with Emerson operations consultant, Chris Forland, on the opportunities for refiners to optimize this energy intensive unit.

Working with engineers in the Rosemount Analytical Gas division, Chris has developed a spreadsheet with fired heater efficiency economic calculations which allows refiners to get a rough estimate of the potential value in applying efficiency solutions like the SmartProcess Heater Optimizer.

You can enter data in the cells with blue text for each fired heater in your plant to get a quick assessment. Chris has filled in typical values from a cross section of refineries in case you don't have exact data. This will let you get a feeling for the overall improvement opportunity and if there is enough return on investment to warrant a closer look.

If you have fired heater units in your manufacturing process, give this calculator a try and let us know what you think by adding a comment or contacting us.

Tags: fired heater | economic calculator | refining | refinery | energy efficiency | heater optimizer |

December 4, 2006 in Energy Management, in Fired Heater, in Process Optimization, in Refining | Comments (2) | Trackback (0)

Emerson's Lou Heavner, a consultant on the Advanced Automation Services team, recently co-authored a paper, Using Neural Network Technology for Virtual Sensing in Crude Refining Units, at the recent ISA EXPO 2006. Lou worked with engineers from Russian oil refiner, Lukoil.

Always a great presenter, Lou not only shared his expertise on this project, but the fact he homebrews beer. I imagine this keeps his control application skills honed!

The paper describes the need to improve quality of refined products to improve upon the current process of taking lab samples once or twice a day. The operators would make changes to the process based upon the lab readings. They tried to control key temperatures and other process variables on the pre-flash, atmospheric, and vacuum columns by manipulating flows including reflux, furnace fuel, pump-arounds and product draws.

To make these quality adjustments more in real-time, the refinery engineers want to use something other than costly and maintenance-prone on-line analyzers. They decided to use neural network technology to build real-time inferential property estimators which could run inside the refinery's existing DeltaV controllers.

Lou work with the Lukoil engineers to build ten artificial neural networks measuring gasoline, kerosene, diesel, VGO, and residue on the pre-flash, atmospheric, and vacuum towers. They believe this application of virtual sensors to be one of the world's largest on a single crude unit.

The real work comes in collecting the data needed to train the neural networks. They needed around 100 lab samples for each model and the continuous historical data for process variables over this sample period. The DeltaV Neural software helped automatically perform the data collection and model training need to build and prove the neural networks. Up to 20 process variables were collected as inputs in training each of the ten neural networks. Any abnormal operating conditions were identifies to exclude the data from this time period from the model. Any of the variables that had little or no effect on the model outputs were eliminated.

The largest challenge in the data collection effort was in the lab data. It had to be accurate in terms of precise time of taken sample and the proper analysis of the sample. The quality of the neural networks is directly impacted by the accuracy of the samples. Another important factor is to make sure the process data is not filtered or manipulated, but instead a raw snapshot.

The resulting inferential sensors predicted what the lab results showed within a few degrees. The ones estimating lighter refined products were more accurate. The engineers have not closed the loop to run the control strategies based on these readings, but they do present the information to the operators to make adjustments more frequently then they could with samples coming only once or twice a day.

Tags: neural network | virtual sensor | lab sample | crude unit | refinery | refining | distillation | ISA EXPO 2006 |

October 31, 2006 in Analyzers, in Distillation Column, in Process Optimization, in Refining | Comments (0)

From my days as a young systems engineer working on offshore oil & gas platforms in the Gulf of Mexico, I know that abnormal situations in our processes are something we all wish to avoid. A 1999 study by the ASM consortium estimated $10 billion USD in losses for U.S. process manufacturers due to abnormal situations. The question is how best to prevent these abnormal situations from occurring in the first place.

Emerson's Ravi Kant and Roger Pihlaja recently presented a paper, "Abnormal Situation Prevention (ASP) in Complex Systems" at the recent NPRA Q&A and Technology Forum.

In their presentation they stress that the potential severity and cost of an incident increases if timely corrective action is not taken. An example cited from a refinery abnormal situation is the failure of a butterfly valve. After going several hours without detection by the automation system or operations personnel, it caused the Cat Cracker (FCCU) to shut down. In a matter of minutes this caused the refinery to shutdown, resulting in more that $1 million USD per day in lost revenue.

Ravi and Roger explained how abnormal situation prevention (ASP) technology embedded in the sensors, actuations, and machinery health are closest to the process and have access to better information. This ASP technology can predict root causes of abnormal situations through high-frequency spectral and statistical data analysis within these smart devices. The main reason for doing this analysis closest to the process is that the sampling frequency is greater--22 samples per second, instead of 1 sample per second to 1 sample per minute typical at the automation system level.

Data analysis at this higher frequency can uncover process anomalies including drift, bias, excessive noise, process spikes, and plugged conditions. Some of the detection and prediction algorithms and techniques which are employed include: polynomial extensible regression, principal component analysis, statistical process control, decision trees, fuzzy logic, and neural networks.

They cited some specific ASP applications in refineries including early detection of catalyst losses, catalyst circulation issues, afterburn conditions, column and heater coking, temperature runaway, and acid levels outside optimal or safe levels. The key to detecting these process conditions is sharing this data analysis at from the field device level, up through the equipment level, up through the process unit level to the operators and plant maintenance staff. Digital communications technologies like Foundation fieldbus and HART provide the information path.

Roger also shared with me other high-frequency data dependant ASP applications in the process including:

• Plugged impulse line detection for DP flow transmitters
• Flame instability
• Stick/slip in FCC solids transfer lines
• Stirred tank vessel agitator diagnostics
• Continuous rotary drum vacuum filter diagnostics
• Fouling & DP level transmitter plugging in evaporators
• Detection of developing ASP issues like arching, bridging, & rat-holing in bulk solids storage vessels
• In-situ proof testing of emergency relief systems

Work continues to refine and extend these predictive ASP technologies to more smart field devices to increase the "eyes and ears" on the process in order to avoid the costs and losses associated with abnormal situations.

Tags: abnormal situation prevention | ASP | refining |

October 26, 2006 in Abnormal Situation Prevention, in Distillation Column, in Fired Heater, in Refining | Comments (0) | Trackback (0)

In an earlier blending applications post, I mentioned some of the advantages of online/inline blending over traditional, batch-based blending. It’s a process which crosses many industries including refining, pulp and paper, chemicals/specialty chemicals and food and beverage to name a few.

I came across an article, Optimizing blending operations by Julie Valentine, a refining specialist in Emerson’s Micro Motion division. Julie notes that for refiners, the motivation for changes to the blend process are in improved control, improved measurement, improved analyzers and improved optimization techniques. One of the keys is high performance flow measurements of the raw materials to precisely control their flow rate as they are blended together. The Micro Motion Coriolis flow meters are extensively used for both the raw material and final blending product flow measurement. Their 0.1% accuracy couple embedded advanced control in control systems like the DeltaV system, enable blend optimization to be done within the control system.

In the article, Julie describes a U.K. lube blending plant which switched from a sequential measurement system to a flow measurement based system. This switch enabled the raw materials to simultaneously flow into the mixing tanks, increasing the throughput of the operation. The accurate measurement of the raw materials meant that the blend would be on-spec as it was filling in the mix tank, and shortened the overall mix cycle, again increasing throughput.

The Coriolis meters also provide high accuracy density measurements, which was important since blend component pipe headers are cross connected and this density measurement can quickly spot and notify operators of cross contamination which can affect the quality of the blend.

One other example Julie cites is where the blending optimization for the blend of gasoline allows refineries to make use of the blend components available from production and choose the blend which will produce the required specification at the lowest cost, while also managing inventory levels.

The accuracy of the flow measurement is critical to the blend optimizer. Julie cites a study where poor flow measurement with 0.3% accuracy translates into lost profitability of up to $200,000 per year for a 100,000BPD facility. This is caused by the blend optimizer making the wrong optimization decisions based upon the inaccurate data it receives.

Tags: online blending | inline blending | blend optimization | flow measurement |

September 13, 2006 in Blending, in Measurement, in Refining | Comments (0) | Trackback (0)

As blogs have brought self-publishing to individuals and their growth has skyrocketed, the next wave--self-published videos--have arrived and even come to our conservative world of process automation.

YouTube and Google Video are two examples of sites where you can upload your videos to share with the world.

I bring this up because I received an email this morning of a video done by Pennsylvania Cable Network with their PCN Tours series. The 52:35 video features a tour of American Refining Group in Bradford, Pennsylvania in the U.S. They are celebrating 125 years as the United States longest continuously operated refinery.

Take a look at 17:45 and 20:15 and you'll see Emerson's DeltaV system in their crude unit control room and reformer control room. It's great that PlantWeb technology could play a role in their success.

As we continue to move forward in this new age of communications, please let me know if your process automation work is highlighted in one of these newer self-publishing mediums like blogs, podcasts, or videos.

Tags: refinery | American Refining Group | crude unit | reformer |

September 1, 2006 in Miscellaneous, in Refining | Comments (0) | Trackback (0)

Many process manufacturers have flow metering stations where ownership of incoming raw materials, intermediates, and/or outgoing products change. This custody transfer process is common with oil and gas producers, refiners, and chemical/petrochemical manufacturers.

Accuracy is critical since these measurements impact the bottom lines for both the seller and buyer. And, with the introduction in the U. S. of the Sarbanes-Oxley (SOX) Act of 2002, companies are required to put the controls in place to prove the accuracy of these measurements. Other countries have similar regulations requiring these documented proof-of-accuracy processes.

Robert Fallwell, a regional manager in Emerson’s Metco Services business, has written an excellent article, Sarbanes-Oxley audits: coming soon in the July issue of Control Engineering magazine.

Robert shares his expertise on how process manufacturers need to prepare for the SOX auditors. He boils it down to:

…they ask for proof that flow measurements are accurate, that you have procedures to ensure measurement accuracy, and that the plant’s operators, engineers, and production accountants have been trained in the correct procedures for the measurement control process.

The article is filled with advice on how to get ready, where to start in your process, and even 9 steps on how to comply with SOX. In addition to the expertise Robert and the METCO team bring to SOX compliance planning, Emerson has well-established flow technology and calibration management software help assure accuracy over time.

If your business is impacted by SOX or similar regulations, you’ll want to incorporate some of the ideas presented in this article.

Tags: Sarbanes-Oxley | custody transfer | regulatory compliance | flow measurement |

August 8, 2006 in Chemical, in Custody Transfer, in Measurement, in Oil & Gas, in Refining, in Regulatory Compliance | Comments (0) | Trackback (0)

As I mentioned in an earlier post on fired heaters in refineries, this is an area where refiners can reduce energy costs by modernizing and optimizing the performance of these units.

The objective is to operate the heater at the lowest fuel cost, while being able to reliably handle the variability in fuel quality and BTU content for any waste fuels used by the heater. Many of these units operating in established markets around the globe are 20 to 30 years old and these often experience unplanned outages due to component failure. Another challenge is the tube coking / fouling in the units which can reduce operating efficiency.

Every 3-5% improvement in fired heater efficiency can mean 3 to 5 cents per barrel net margin improvement. For a 100kbpd facility, this translates into $1.8 to $2.9 million USD in annual savings.

I spoke with Chris Forland, an operations consultant for the Emerson Process Management group. Chris and the other consultants have helped refiners identify several ways to improve the efficiency and reliability of their fired heaters.

It starts with a study to baseline the performance and to confirm the operating issues impacting performance. This study helps to identify opportunities for improvement and to provide estimated costs and benefits to determine return on investment for the improvement initiative.

Beyond the SmartProcess Heater Optimizer mentioned in the earlier post, some typical opportunities Chris sees for improvement include on-line continuous measurement of fuel quality and BTU content, in-situ measurement of oxygen and carbon monoxide in the exhaust stack, predictive diagnostics for the smart instrumentation, digital valve controller actuator for the damper drives and control valves, and predictive measurements around the flame and relative coking.

These projects usually include a post project audit to determine the actual return on investment versus that forecasted one in the front-end study. This provides a measurement for the success of the project by determining the actual return on investment.

Tags: refinery | refining | fired heater | energy efficiency | process optimization |

June 23, 2006 in Fired Heater, in Process Optimization, in Refining | Comments (4)

Recently an email came in that said Refineries and Petrochemicals specialist, Ravi Kant, and ASP Validation and Verification Engineer, Ahmad Hamad, in our Performance Technologies division, won the Fuels & Petrochemical’s Award for best paper (out of more than 80 papers) at the AIChE 2006 Spring National Meeting.

This was something I had to get my hands on and find out why, and extend hearty congratulations to Ahmad and Ravi. The predictive PlantWeb technologies developed by this team find their way into AMS Suite software products, Rosemount transmitters, and other Emerson smart field devices.

With many industries like refining and petrochemicals running near full capacity, abnormal situation prevention provides a method for early detection with problems in the process and provides an opportunity for timely corrective action--before down time, quality issues, or even safety issues occur.

The paper, Advances in Abnormal Situation Prevention in Refineries and Petrochemical Plants, looks at traditional ways of preventive maintenance and the drawbacks in performing unnecessary maintenance, sometimes requiring down time, and being unable to detect abnormal situations.

It also explores other techniques for abnormal situation management. These solutions use knowledge-based diagnostics with data drawn from the continuous historian to develop a multivariate model. The source data from the historian is typically very low frequency from once per second to once per minute. This approach fails to detect abnormal situation which can develop rapidly. It also often fails to find problems with machinery, devices, and transmitters in the process. An example might be a stuck valve.

Ahmad and Ravi describe how advances in microprocessor performance and digital communications like Foundation Fieldbus and HART make it possible to do high frequency diagnostics within smart field devices. Emerson Process Management has developed Abnormal Situation Prevention (ASP) blocks in smart field devices like Rosemount 3051s transmitter, which capture high frequency process data at 22 samples per second. The blocks perform statistical, frequency-based, auto-regression, wavelets and other diagnostic measures to try to discover problems in the process in their earliest stage. And automation systems like the DeltaV and Ovation systems can turn the most critical of these alerts from these ASP blocks into operator and maintenance alarms for corrective action to begin.

The paper describes for cases where this early detection can prevent abnormal situations from occurring. These include: coke detection in refineries, catalyst circulation in fluid catalytic cracking (FCC) units, maltrays detection in crude columns, and gas turbine abnormalities. These are but a few of the critical applications where abnormal situation prevention technology can be applied.

Like anything else, the closer you can get to the source of the abnormal situation, and the earlier you can identify it, the sooner you can mitigate or prevent the situation from occurring.

Tags: refining | petrochemicals | AIChE | statistical control | multivariate analysis | abnormal situation prevention | ASP |

May 2, 2006 in Abnormal Situation Prevention, in Refining | Comments (0) | Trackback (0)

You don’t have to look too hard to find news stories (here, here, here) of rising oil prices and their impact on process manufacturers around the globe.

Refineries and petrochemical manufacturing processes can especially require vast amounts of energy to process the feedstocks into intermediate or final products.

I spoke recently with Doug White, who leads our advanced automation services consultants for Emerson Process Management. Some of the folks I’ve written about like James, Eric, and Lou are senior consultants in Doug’s organization.

Doug mentioned that one of the units at which refiners and petrochemical manufacturers should take a close look is the fired heater which provides the required heat for the distillation process. In many plants, these units were built 10-15 years ago or more. Most were built in times when natural gas was extremely inexpensive. There was little need for energy efficient designs—so even today they consume energy at higher rates than they could.

He sees these units as a quick way for manufacturers to save costs and improve their bottom lines.

Doug described these opportunities and gives very practical advice on how to get the project assessed, implemented, and sustained in an Oil & Gas Journal article entitled: Advanced automation technology reduces refinery energy costs. Some steps Doug recommends from the assessment phase:

1. Data gathering. Compile information about existing systems.
2. Interviews with plant staff. Find current energy-use problem areas, understand current operational procedures, and stimulate ideas on possible changes.
3. Evaluation of plant energy economics. Understand what are the major users and their costs of operation.

Doug's team has packaged some of their expertise coupled with advanced control software into a SmartProcess Heater Optimizer application.

If you are one of the manufacturers struggling with higher energy costs, this article is well worth the read to develop a plan to reduce these high energy costs.

Update: Repaired broken hyperlinks.

Tags: refining | petrochemicals | fired heater | distillation | project assessment | project implementation |

April 3, 2006 in Energy Management, in Fired Heater, in Refining | Comments (0) | Trackback (2)

One of the guys who has been around as long as I have is Lou Heavner, a Consultant in our Advanced Applied Technologies team. As an MIT graduate, he is one really sharp person, and also someone who can simplify and communicate complex ideas.

I asked Lou about what he's been working on recently in the world of applying advanced process controls for process manufacturers. Lou's been recently involved in a project in the European region where he consulted with a refiner to get DeltaV PredictPro working on a crude unit and to take advantage of its optimization capabilities.

During that project, it became clear to Lou that the term optimization conjures up a different vision for most people. He was confronted with matching the capabilities of the system to the expectations of the customer and needed to dive pretty deeply into how optimization works in the DeltaV software, just to explain and train the local refinery staff.

The crude unit is the first unit in a refinery and the fractionator is a great place to start applying APC technology. An optimizer can take advantage of the capabilities of PredictPro and the extra degrees of freedom in a typical crude unit to drive the process to maximum throughput and/or minimize the energy required (as fuel in the fired heaters.) It can also support an objective to maximize yield of the more valuable cuts (products). The system is working and delivering benefits, but Lou is doing further work to quantify the results and turn this skeptical refiner into a good reference site.

Applications like these have been developed for many industries and branded as SmartProcess Optimization application packages. Some of these applications for refineries include the Fractionator Optimizer and the Heater Optimizer packages.

As one who applies the advanced process control software on a regular basis, Lou's feedback to the Emerson technology development teams has enhanced the software over a number of releases.

Lou is a regular presenter at the Emerson Exchange sharing his expertise with the Emerson customers who attend. This year he'll be presenting a short course which explains how to implement optimization with DeltaV PredictPro and surveys several optimization techniques including non-linear searches for minima, load allocation, and LP (linear programming) solutions. These optimization techniques are a key and inseparable part of those who employ use model predictive control techniques.

Tags: model predictive control | advanced control | linear programming | Emerson Exchange | refining | fractionator | heater |

March 23, 2006 in Crude Unit, in Refining | Comments (0) | Trackback (2)

I heard a story of a tremendous effort from our advanced applied technology experts to help a refiner stabilize his units after a restart. I went to the source, James Beall who is the team leader of our U.S. Control Performance team. It turns out that he is the one responsible for these successful efforts.

He received a call from one of our local business partners late on a recent Wednesday night to assist this U.S. based refiner with the startup of some of their units including three new distillation columns. This refiner had battled the startup for several days but could not stabilize the unit or produce on-spec product. The unit would have to be shut down within 48 hours due to limited storage if stable operation and in-spec product could not be achieved.

James arrived early Thursday morning and began to assess the situation. He installed Emerson’s EnTech Toolkit to be used for process control diagnostics, complex loop dynamics identification and advanced loop tuning. He reviewed the situation with the customer, prioritized and set the order of tuning of the control loops around the columns. Using DeltaV Tune and the EnTech Toolkit to provide coordinated loop tuning, the columns were stabilized by 10:30 pm, 12 hours after arriving on-site.

During this process, several measurement problems were identified and plant personnel began to troubleshoot and correct the problems. Once these instrumentation problems were resolved within a few days, James returned to the site to continue the control performance improvements.

The quantified results? The columns achieved 100% of design production 5 days after James first arrived and nearly another 20% after a total of 7 days.

Based on these results, the refiner is looking to set up a continual process performance improvements program with James and the Control Performance team.

Tags: refining | variability | distillation column | loop tuning | control performance |

March 9, 2006 in Distillation Column, in Refining | Comments (2) | Trackback (2)

A recent post on Control.com's global on-line community for automation professionals asked about how to go from pneumatics to a process automation system in a refinery.

Given the strong global demand for refined products, refineries want to avoid any downtime when modernizing their automation and safety instrumented system technologies. This process of cutting over from old to new while the process is running is called a Hot Cutover.

Ken Suetterlin, a senior Emerson Project Manager from our Refining and Chemical Industry organization responded to the Control.com post with the following recommendations:

1. Identify which loops are to be converted as Hot Cutover and which are to be done during Turnaround. If possible, we recommend you convert loops related to safety shutdowns during Turnaround. The loops in each category can be color coded on P&IDs and/or indicated by category in an instrument database. Then you can sort by Hot Cutover and get a list of all loops in that category.

2. Once you have a list you'll want to work closely with Operations to schedule the loops based on production priorities and loop complexity.

3. Install and test as much as possible in advance to avoid last minute surprises.

Upfront planning is critical to avoid downtime. In a Shell Deer Park refinery modernization project, Emerson supplied all phases of engineering services related to Hot Cutover including conceptual design, FEED (Front End Engineering Design), detail design, FAT (Factory Acceptance Test), field commissioning, SIT (System Integration Test), SAT (Site Acceptance Test), and installation. The team worked closely with installation contractors, and provided the engineers and technicians for actual cutover.

The Hot Cutover process at the Shell Deer Park refinery which included critical units like the Cat Cracker is described in an April Hydrocarbon Processing article.

Here is a description of some of the expertise and technologies Emerson applies in a Hot Cutover project.

Tags: Project Services | Hot Cutover | refining | FEED | Turnaround |

March 1, 2006 in Project Services, in Refining | Comments (0)