Analyzers

Last week, I highlighted some of the greenhouse gas regulatory reporting changes happening here in the U.S. I checked in with Emerson's Doug Simmers, a global product manager in the Rosemount Analytical Combustion Gas business. Although alternative sources of energy continue to grow as a percentage of energy consumed, combustion of oil, gas, and coal is forecasted to be the dominant source of the world's growing energy needs for the next several decades.

Carbon Dioxide (CO₂) is a natural byproduct of combustion. Unless a technological breakthrough in usable energy production occurs, combustion and its greenhouse gas byproduct will be with us for quite a while. Doug stressed to me that combustion flue gas analysis is key in reducing greenhouse gas emissions. Some sources of energy such as natural gas with its high hydrogen (H₂) content produce much less CO₂ than other sources such as coal, which has a higher carbon content.

For process manufacturers with utility boilers, industrial boilers, kilns, process heaters, catalyst regenerators, incinerators, etc. maximizing combustion efficiency minimizes greenhouse gas production.

The relationship between excess air, carbon monoxide (CO), CO₂, and oxygen (O₂) is shown in the graph. Doug notes the counterintuitive point that maximum combustion efficiency actually occurs at the point of maximum CO₂ production. Less energy is produced as you move away from this point, which in turns causes the combustion process to run longer to produce the same amount of energy. The extra time required actually means more CO₂ is produced.

To operate in the chart's "blue box" area of maximum combustion efficiency, it's important to continuously analyze the flue gas and close the fuel/air ratio control loop. Unfortunately, the flue gas does not always have a homogeneous distribution, especially when multiple burners are involved.

Doug described how a stratification profile is developed using an array of Oxymitter transmitters of varying lengths. This array is used for balancing the burners, detecting burner fouling, discovering poor fuel distribution and spotting variations among units. He noted how the oxygen probes made with zirconium dioxide (ZrO₂) "fuel cell" technology were well suited since they operate well at elevated temperatures, which permit an in situ (in place) design.

The output from these ZrO₂ sensors is inverse, and logarithmic. This means that the signal increases at the low O₂ levels commonly experienced in combustion processes. The accuracy is stated as a "percent of reading", as opposed to the conventional "percent of full scale". The accuracy actually improves at lower O₂ levels. The life of these O₂ cells can exceed 3-5 years, depending on sulfur levels, which shorten the cell's life.

Given that fossil fuel combustion will be around for a while, maximizing the efficiency of this combustion helps minimize greenhouse emissions. The place to begin is with better flue gas analysis to maximize heat rate, balance la rge furnaces, and diagnose operational problems.

MP3 | iTunes

I always like receiving goodies that I can pass along to the readers of this blog. This one, a boiler blowdown savings calculator, is from Emerson's Jim Thompson. Jim is a manager in the Rosemount Analytical Liquid business.

Jim probably knew that I'm definitely not a boiler blowdown expert, so he was kind enough to point me to a great Boiler Blowdown Fact Sheet developed by the North Carolina Division of Pollution Prevention and Environmental Assistance.

For those like me who may have heard the term but not have known what it really meant, the fact sheet sums it up:

To avoid boiler problems, water must be periodically discharged or "blown down" from the boiler to control the concentrations of suspended and total dissolved solids in the boiler. Surface water blowdown is often done continuously to reduce the level of dissolved solids, and bottom blowdown is performed periodically to remove sludge from the bottom of the boiler.

It makes good economic sense to perform these blowdowns. You can reduce fuel consumption, use less chemical treatments, and reduce heat loss in the steam you are producing. Automated boiler blowdown operations can:

...save about 2 percent of a facility's total energy use with an average simple payback of less than one year.

Jim pointed out that measurement of boiler feedwater conductivity is an indicator of the concentration of dissolved solids. Rosemount Analytical conductivity sensors, transmitters, and analyzers are used to measure for these dissolved solids. A blowdown system typically includes an automation system running the PID loop, a sample cooler and downstream conductivity sensor & transmitter as the loop input, and the blowdown control valve position as the loop output. Depending on the boiler application, conductivity, pH, dissolved oxygen and free chlorine may also be measured and controlled on the feedwater and makeup water lines.

The calculator uses the maximum recommended concentration limits according to the American Boiler Manufacturers Association (ABMA). Also, the American Society of Mechanical Engineers (ASME) has developed a best operating practices manual for boiler blowdown. The recommended practices are described in Sections VI and VII of the ASME Boiler and Pressure Vessel Code. The calculator helps you identify energy-saving opportunities by comparing your blowdown and makeup water treatment practices with the ASME practices.

If your process includes boilers, give the calculator a try to see if you have opportunities to improve efficiency and reduce ongoing maintenance costs.

MP3 | iTunes

My trusty Emerson RSS feed alerted me to a new liquid analysis application guide for the metals extraction and processing industry. This guide, published by Emerson's Rosemount Analytical Liquid business, covers the liquid analysis solutions for the following processes: leaching and ore extraction, concentration and separation, finished product purification and waste disposal.

I confess to knowing little about mining and metals production processes, but after reading this application guide, I have a much better appreciation for the processes and some of the challenges.

In the leaching and ore extraction section, precious metals such as gold and silver are commonly processed using cyanide compounds. As the guide succinctly puts it:

Cyanide dissolves the precious metal by forming a chemical complex with it, thus separating the precious metal from the other constituents of the ore, which do not dissolve.

The challenge is accurate pH measurement for both safety and efficiency. pH values lower than 11 favor the formation of hydrogen cyanide gas, which is colorless, odorless and poisonous in sufficient concentrations. Continuous pH monitoring and control is critical to prevent the formation of this gas. Measuring pH is complicated by the finely ground abrasive ore which can coat and abrade the pH sensor. The guide describes the Rosemount Analytical pH probe, jet spray cleaning attachment, and measurement analyzer best suited for this application.

The guide has easily understandable process flow diagrams for flotation cell pH measurement, titanium dioxide (TiO₂) manufacturing, Alumina/caustic ratio control, steel treating, cyanide oxidation and waste disposal via scrubbers. It provides a good overview description of the process, the measurement and control challenges, and the liquid analytical products that can help address these challenges.

If you're in the metals extraction and processing industry, this guide helps you understand the products Emerson offers for a particular application. For those of us not familiar with the industry, the guide provides a great overview of some of the major production processes and the challenges engineers from this industry face.

Given recent trends in metal prices, if you're a process automation engineer or engineering student, you may want to learn more about this industry. If you do, you'll find this guide to be a useful overview.

Interphex2008, the Pharmaceutical and Biotech manufacturing conference is going on this week in Philadelphia. Before Emerson's Terry Blevins and Mike Boudreau left, they passed along the presentation they are giving on Thursday, March 27. It's entitled, Application of PAT in Product Development. They are joined by University of Texas at Austin PhD graduate student, Yang Zhang and Broadley-James' Trish Benton. Here's an excerpt from the abstract:

The Process Analytical Technology, PAT, initiative encourages innovation in pharmaceutical development, manufacturing, and quality assurance to enhance understanding and control of the manufacturing process. The challenge for many manufactures is to identify how best to address the opportunities that PAT offers. Broadley James, Emerson Process Management, and the University of Texas are working together to examine and quantify the potential to reduce cycle time and out-of-spec product through the use of high fidelity, dynamic simulation and multivariate analytics. The objective of this work is to show that the impact of PAT can be maximized through the integration of these tools during product development (PD).

In the presentation, Terry begins by discussing the U.S. Food and Drug Administrations' PAT initiative, which has a framework that identifies some of the tools they discuss in the presentation. These include:

• Multivariate data acquisition and analysis tools
• Process and endpoint monitoring and control tools
• Continuous improvement and knowledge management tools

Terry describes on-line process analytics including fault detection and quality parameter prediction. Tools for detection of abnormal operations vary for measured and unmeasured disturbances. For measured disturbances, principal component analysis (PCA) captures contributions that can be associated with process measurements. Deviations may be quantified using Hotelling's T-square statistic.

The residual space that is not captured by the principal component score space reflects changes in unmeasured disturbances that can impact operations. These deviations can be measured with the Q statistic, squared prediction error (SPE).

For the quality parameter estimation, detection of deviations is addressed using projection to latent structures (PLS).

Armed with these statistical tools, Mike shows how the basis for bioreactor process modeling. In the book Mike coauthored with Greg McMillan, New Directions in Bioprocess Modeling and Control, they present a first principal bacterial model that was developed for fungal, bacterial, and mammalian cell processes. The intent of the process model is to more quickly evaluate input step techniques and control strategies in the PD stage.

The BioProcess International magazine article, PAT Tools for Accelerated Process Development and Improvement describes this collaborative effort between Emerson and Broadley-James technologists and University of Texas researchers along with how these tools can accelerate life science manufacturers' PD phase.

A great question came in on the Operating Fired Heaters More Efficiently and Reliably blog post:

Jim I work with natural draft heaters on a daily basis and have initiated several efficiency tests with improved burner internals. I am looking for an opportunity to optimize dual firetube treater by first off improving the combustion efficiency to 80% in each tube and then staggering the temperature controls so that one tube runs 90 to 100% of the time and the other tube only fire during high load requirements.

I sent the comment around our advanced automation consultants for any comments that they might have and I received a great reply from Lou Heavner whom you may recall from earlier posts. Lou describes how to approach optimizing these heaters:

Heater efficiency is calculated using heat loss or input/output method. Input/Output method is difficult because you have to account for lags and delays between fuel firing rate changes and the measurement of process heat absorption changes and in the specific case where there is incomplete phase change on the process side (e.g. partial vaporization) you cannot easily solve with reasonable instrumentation. The heat loss method measures heat loss in the flue gas and assumes any other losses are negligible and constant. If not, they need to be measured and added as well.

Heat loss requires knowledge of the supply air (and fuel) temperatures and the flue gas exhaust temperature as well as the composition of the fuel and flue gas, just like with a boiler. In perfect combustion, there would be no unburned fuel in the flue gas and no sensible heat losses. But due to practical considerations, there are sensible heat losses and to calculate them, you need to know the delta T between the exhaust and ambient and how much excess oxygen remains in the exhaust. Efficiency calculations made using this technique can be pretty accurate in a natural draft heater, but if there is air leakage after the combustion zone, tramp air will show up as lower efficiency due to increased O2. And there is usually an optimum cost operation where the trade-off between sensible heat losses and unburned fuel losses require some level of unburned or incompletely burned fuel leaving in the flue.

When you are ready to control, the goal is to minimize excess O2 while not allowing excessive fuel to go unconsumed. CO analyzers are often used to detect incompletely burned fuel and the goal is usually to keep it below 150 ppm or some lower target. O2 is controlled to stay as low as possible without exceeding the CO limit, which is usually 2% O2 or less for the fluegas.

You can do this with simple feedback control, but feed forward control can help do better. Information on fuel quality, if it varies, and process side temperatures and flows (the heater load demand) can be used to adjust the fuel and air for combustion to meet the heating demand at maximum efficiency. Fuel and air cross limits are often used to maintain fuel and air ratio without getting into a fuel rich condition in the firebox during load changes. But airflow is usually difficult to measure. Therefore, it is often inferred from damper position.

When evaluating an application, we would want to know what instrumentation already exists and what the process variability looks like. What efficiency are they currently obtaining? Then we would look at the control valves and any other contributors to variability to see if they warrant repair or replacement. We would similarly evaluate the instrumentation and analyzers to see if they need anything there.

Then we could evaluate the control strategy and performance and recommend reconfiguration or tuning as appropriate, which may include advanced process control (APC). The person evaluating the controls would have to weigh the cost against the improvement from better loop tuning, valve repair/replacement, CO analyzer, etc. to come up with the best solution. Dampers are often the weak link in fine control of a natural draft heater.

As my colleague Doug Simmers in Emerson's Rosemount Analytical business noted, "The commenter is probably correct with the strategy to fire one heater full out, and bring the second unit on only when needed. Running at full fire develops the best turbulence for fuel/air mixing, and the excess O2 can be kept lower." This is a load allocation problem when two heaters are firing simultaneously. If we can model heater efficiency for each heater as a function of load, then we could optimize the load allocation across both heaters when both must be fired. Actual testing would identify the models, uncover the best strategy, and verify or disprove this assumption."

He may also be interested in the efficiency calculator, developed by Doug's team.

Join the conversation and add a comment if you have experience to share.

Process manufacturers continue to seek ways to improve their energy efficiency, due to the high cost of energy. Corrosion and solids deposition in boilers, condensers, and steam turbines reduce the efficiency of this equipment and increase energy usage. This can also lead to unscheduled downtime if the conditions persist long enough to cause equipment failure.

One important way to minimize corrosion and the formation of solid particles is to have ongoing, accurate and reliable pH control in the boiler water, boiler feedwater and steam condensate, and main steam (carryover.)

The challenge is that these applications are often very low in conductivity. This is a challenge for continuous pH measurement due to the unavoidable formation of liquid junction potentials in the reference sensor. These cause offsets and instability in the pH measurement.

Emerson's Brian LaBelle, a power industry manager for Rosemount Analytical liquid analytical devices, explained these junction potentials are caused by spontaneous migration of ions from more concentrated to more dilute solution within a pH sensor electrode. What happens is a charge separation occurs among the various ions present. (At the word "ion", my mind raced back to those repressed memories of college chemistry lectures...)

Sometimes a severe junction potential occurs when there is an imbalance of negatively and positively charged ions across the liquid junction found in the basic reference electrode. The lower the porosity of the junction, the greater is the charge separation across this junction.

Sounds like we've gone a long way from the original problem of keep the equipment from corroding and being gummed up with solid particles.

Brian brought me to the solution by explaining that the technology team came up with the solution of replacing the diffusion junction with an open capillary (that's a hole for most of us.) Actually, this is not new or innovative, but what is innovative is that precise, laser drilling on a micro-scale of tens of microns is far more precise than what can be achieved with a twisting, mechanical bit. To minimize the junction potentials and provide more accurate measurement, the optimum capillary is laser-drilled at 25 microns in diameter. This capillary is also tapered outward to the outlet filter to help avoid clogging.

As we depart the micro world of ions and laser holes and return to our world of boilers, condensers, and steam turbines, the pH measurement with the Rosemount Analytical 3200HP pH sensor provides more accurate and reliable continuous measurement to ward off corrosion and solids formation. This means more reliable, efficient operations for this energy-consuming equipment.

Automation World magazine recently had a great primer article on electronic device description language (EDDL) entitled, Device Descriptors Prove Merit. Application manager, Jim Gray, in Emerson's Rosemount Analytical Liquid division best summarized this important standard by saying:

...the most important thing about electronic device description language (EDDL) is that it makes managing process instrumentation easier.

If you're unfamiliar with EDDL, here's a short summary from an earlier news release:

An international standard -- IEC 1804-3 -- Electronic Device Description Language (EDDL) is a universal interface to diagnostic, real-time and asset management information contained in what is currently a growing installed base of more than 20 million field instruments from a host of manufacturers. With EDDL, a user can calibrate instruments, diagnose problems, provide data for user interface displays, identify process alarms, and obtain information needed for high-level software, such as MES, UI/SCADA, plant historians, asset management and ERP.

Virtually every vendor of process control systems worldwide supports the standard language and the information it describes is available in any HART Communication, Foundation fieldbus, or Profibus based instrument made since 1990.

ModelingAndControl.com's Terry Blevins is heading up the ISA-SP104 standards committee to continue to advance the EDDL standard.

I asked Jim for some examples of how this standard makes thing easier for automation engineers, operators, and maintenance technicians. As Jim sees it, the biggest advantage is that the presentation of the diagnostic and other information in smart field devices is separated from the actual data. This allows software applications to present information from a host of different device suppliers in a common, intuitive way.

The best analogy I can think of is RSS where the data resides in XML files on various websites across the internet. RSS Readers like Google Reader, Internet Explorer 7, Firefox, etc. handle the presentation of this information each in their own unique way. As a consumer of RSS feeds, it's much faster and easier to read the feeds in a common location in a common way with one of these RSS readers.

In the case of Rosemount liquid analytical smart devices like pH, conductivity, and dissolved oxygen transmitters provide EDDL files with their diagnostic, configuration and operating data and make this data available to software packages like AMS Device Manager to present the information. Like the RSS readers, AMS Device Manager presents this data in a standard way including device status, trends, gauges, and advanced device help to name a few. This is true for any suppliers' devices which support the EDDL standard. Also, other application software which supports the EDDL standard can present this information from Emerson devices which support this standard.

Jim sums it up rather nicely in the article:

The whole idea is to let the user know what is going on with the device and any actions that need to be taken, quickly and clearly, and to make configuration commissioning easier.

The growing conversation on the Food and Drug Administration's Process Analytical Technology (PAT) initiative continues. My persistent RSS search on PAT pointed to another great article, this time in Pharmaceutical Technology magazine. The article, The Five Steps to Starting PAT by Jacob Cook, discusses simplifying the process of getting started with a PAT initiative.

The five steps discussed were:

• Pick simple.
• Understand all the details and nuances.
• Evaluate the instrumentation you already have, and the information you can easily collect.
• Understand the appropriate intervals for collecting that data.
• Evaluate the tools available for reading and synchronizing the data.

Just last week we discussed the benefits of applying a structured approach to a PAT initiative to improve opportunities for initial success.

I passed this article by Christie Deitz, whom you may recall from earlier posts on PAT and ISA-88 (S88) projects. Like most initiatives, Christie believes having good data (step 3) is very important. The Life Sciences industry project teams use DeltaV Batch which integrates in a single location the data required for this analysis. This data includes: alarming, continuous and batch history, operator actions and other events. Having this information organized together around batches and campaigns can help identify PAT opportunities.

Where manufacturing execution systems (MES) like Compliance Suite are also used, exception-based reporting can also help with this process of analyzing the data. We discussed using XSL style sheets to do these reports in an earlier post. An example of this exception-based reporting is showing the batch reviewers only the alarm data that occurred during any particular batch run or campaign.

Christie also points out that where manufacturers have already implemented PAT analyzers, they can make decisions in electronic work instructions (EWIs) based on the analyzers' real-time data values to help verify its correct operation. For example, if a PAT analyzer is not reading the expected value based on other operating data, the work instruction can be to have the operator take a manual sample against which to compare the analyzer data value.

Whether you "pick simply" as a starting point or apply a structured methodology to assess the best opportunities to begin, analyzing your existing data is extremely important. The analysis process is less manually intensive when this data is either centralized or logically organized together in some manner to help better identify these opportunities.

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.

One of the workshops that caught my eye at next week's Emerson Exchange meeting is Dave Anderson's Dissolved Oxygen Measurements Improve Beer Quality and Lower Operating Costs. Dave is from Emerson's Rosemount Analytical division. This probably caught my eye because I'm a fan of quality beer.

Dave helps us novices with the basics like what dissolved oxygen actually is. It's the concentration of Oxygen (O2) in liquid phase remaining after exposure of gaseous oxygen to an aqueous solution. The process of brewing is aerobic where the yeast requires oxygen to convert the sugars to ethanol in the fermentation process. The dissolved oxygen measurement is important since too much oxygen can create unwanted side effects, including excess Dimethyl sulfide. This compound negatively impacts the beer's taste.

The Rosemount Analytical Dissolved Oxygen sensors are designed to handle high pressure surges and not be as sensitive to flow rates. Most oxygen sensor can handle a few clean-in-place (CIP) operations which clean and sterilize the process vessels and piping. This sensor was designed to handle more than twenty of these CIP cycles.

Dave mentions the key process areas where brewers should measure dissolved oxygen. These areas include: brewhouse wort kettle, fermentation/aging tank, de-aerator vessels in the packaging area, and the utilities. Also, it is important to develop best maintenance practices to maintain highly accurate measurements over time.

Better control of the dissolved oxygen levels throughout the brewing process has great impact on the quality of the beer produced. And that makes us global beer consumers happy indeed. I hope to see a few of you next week in Nashville!