Process Solutions from Wet Chemistry to Near-Infrared Spectroscopy
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Introduction to Metrohm-Applikon, Overview of Process Analysis, Applikon Process Analyzers and Example Applications
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Process Solutions from Wet Chemistry to Near-Infrared Spectroscopy
- 1. Process Solutions from Wet Chemistry to Near-Infrared Spectroscopy – Ensure Product Quality & Prevent Downtime
- 2. Tim Deschaines, Ph.D. Product Manager – Applikon Group Based in Riverview, FL tdeschaines@metrohmusa.com Introduction
- 3. • Introduction to Metrohm-Applikon • Overview of Process Analysis • Applikon Process Analyzers • Example Applications Overview
- 4. • Global HQ in Schiedam, Netherlands • Development, Production, Global Distribution • North America Operations • Tampa, FL • Analyzer Assembly Operations • Application Support • Technical Support • Houston, TX • Demos & Training • Technical Support • Service Operations • Application Support • Toronto, Ontario • Sales Operations • Service Operations Metrohm-Applikon
- 6. • Online Process Monitoring and Control Process Analysis
- 7. • Lab Analytics • <1% RSD • Response time – hours • Advanced technology • Professionally maintained • Clean Environment • Process Analytics • <5-10% RSD • Response time – mins, secs • Simple technology • Reliability • Low maintenance • Harsh environment • Safety Process Analysis Typical Laboratory Typical Production
- 8. • A wide array of methods available for on-line, in-line, and at-line analysis • Most common techniques: • Titration • Ion Selective Electrodes • Dynamic Standard Addition • Photometry/Colorimetry • Voltammetry • Near-Infrared (NIR) • From % to trace (parts per trillion) Analysis Methods
- 9. Applikon Analyzer Options ADI 201Y - Online ADI 2045VA - Online Fully integrated solutions ADI 2045TI - Online ADI 2045PL - Atline ADI Alert - Online
- 10. Crude Oil Processing Applications Applications • NH3/H2S in SWS • KF in Crude Oil • TAN in Oil • Salt in Crude Oil
- 11. • “Sour water” – water that contains sulfur and ammonia • Formed when H2S is liberated in crude oil units during the refining process. When H2S dissolves in water sour water is the result. • Reuse or disposal of sour water requires removal of sulfides and ammonia. Sour Water Stripping Sour Water with NH3 & H2S Stripped Sour Water without NH3 & H2S Sour Water Stripper Sour/Acid Gas Removal
- 12. Analyzer for sour water monitoring Typical configuration Dual Vessel: Left NH3 Right H2S Analysis Range: NH3: 5-100 ppm H2S: 0-50 ppm Methods: Sulfide determined by precipitation titration with silver nitrate S2- + 2Ag+ Ag2S Ammonia determined by Dynamic Standard Addition titration Sour Water Analysis
- 13. • Sodium/potassium Analysis indicates process conditions in a chlorine scrubber • A single upset can lead to: • Loss of up to $100,000 of raw material • Product that is off-spec and can’t be sold • Reprocessing of product – costs $$$ • Example plant – 4 upsets per year • Compare losses to cost of an analyzer and sample conditioning system Caustic Scrubber
- 14. • DCS Flow Control Caustic Scrubber A Chlorscrubber DCS Flow Control 0 1 2 3 4 5 6 7 8 9 03/07/200916:00 03/07/200918:30 03/07/200921:00 03/07/200923:30 04/07/200902:00 04/07/200904:30 04/07/200907:00 04/07/200909:30 04/07/200912:00 04/07/200914:30 04/07/200917:00 04/07/200919:30 04/07/200922:00 05/07/200900:30 05/07/200903:00 05/07/200905:30 05/07/200908:00 05/07/200910:30 05/07/200913:00 05/07/200915:30 Date/Time Caustic% Actual Caustic Average = 4.81% Actual Caustic Standard Deviation = 1.61 Chlorscrubber DCS Flow Control
- 15. • ADI Control – Metrohm-Applikon Caustic Scrubber A Chlorscrubber Titration Control 0 1 2 3 4 5 6 7 8 9 21/07/200911:45 21/07/200914:15 21/07/200916:45 21/07/200919:15 21/07/200921:45 22/07/200900:15 22/07/200902:45 22/07/200905:15 22/07/200907:45 22/07/200910:15 Date/Time Caustic% Actual Caustic Average = 2.97% Actual Caustic Standard Deviation = 0.43 Chlorscrubber Titration Control
- 16. Process Optimization – Improve quality, speed, safety, less waste, less variance, and save money DCS Average: 4.8% Caustic ADI Average: 2.9% Caustic (Now reduced to 2.0%) Cost Savings: Approx. $1,000 a day savings Leads to $200,000 a year savings (4 on, 2 off schedule) Caustic Scrubber
- 17. Summary: Process Analysis Overview Customizable analyzers: on-line and at-line Range of Diverse Applications Process Optimization Thank you for your attention to the Applikon part of the webinar, up next NIR applications. Lucy J. Thurston, from Marathon Petroleum Company, will now talk about their Process NIR systems and applications Conclusion
- 18. Fueling Efficient Analysis: An Overview of the Use of NIR in the Marathon Petroleum System Lucy J. Thurston
- 19. 19 Laboratory Installations Laboratory units installed at all 7 refineries – Measure RON/MON on finished gasoline & component streams – Aromatics – Olefins – Benzene – Cetane – Fatty Acid Methyl Esters – CORE Aromatics – Ethanol Percentage
- 20. 20 Measured Property ASTM Method Length of Analysis Octane Number (RON/MON Knock Engine) D2699/D2700 1 hour each Aromatics (GC/MS) D5769 11 min per sample ~ 1 hour due to QC in lab Olefin (FIA – Fluorescent Indicator Analysis) D1319 1 hour Benzene (GC) D3606 30 min Cetane Number (Cetane Engine) D613 1 hour FAME, vol % (FTIR) D7371 N/A CORE Aromatics 1 hour + data processing Ethanol, vol % (GC) D5599 25 min
- 21. 21 2 MINS VS 4 ½ HOURS NIR vs Routine Gasoline Certification
- 22. 22 Knock Engine Room NIR Set-Up in Knock Engine Room Knock Engine Room
- 23. 23 Gasoline Certification Lab NIR Set-Up NIR Workstation System I Unit: Still in Use
- 24. 24 Spectral Deviations Between 84 & 91 Octane 84 Octane 91 Octane
- 25. 25 Basis of NIR Equations Original NIR equations built from over 700 samples nearly 15 years ago – In process of replacing 2 DOS-based units (only 1 left!!) Still using same equation – Slope & bias adjust with updated sample set every 4-6 months
- 26. 26 On-line Installations 6 Refineries are currently using on-line NIR for process control – 3 very successful – 3 just starting up within last 2 years With on-line analysis able to achieve blend optimization – Blend closer to targets – Minimize give away – Maximize profit
- 27. 27 Daily analyses of component tanks Daily analyses of unit run down streams Information downloaded into blend program Gasoline blend recipes generated from program based on tank inventories Recipes updated based on analysis of finished gasoline Pumper has ability to “tweak” blends based on real time data from on-line analyzers
- 28. 28 Overview of Blend Optimization Process
- 29. 29 On-line NIR New on-line NIR with sampling condition system
- 30. 30 Oversight Program Acceptance of NIR results provides for more immediate recognition of potential issues Over 4 years worth of NIR & Knock Engine analyses used to show the predictive abilities of NIR with r2 values of 0.983 For any gasoline with NIR (R+M)/2 greater than 0.3 below pump value, knock engine testing is required To maintain equation, calibration sets are updated every 4-6 months
- 31. 31 Oversight Program: NIR Technology Savings to Marathon Totals as of 6/2010 Time per NIR Time ASTM/IR Test NIR Octane 1436 2 min 1 hour NIR Cetane 900 2 min 1 hour NIR % Biodiesel 151 2 min 30 mins ~83 hours for NIR vs ~2400 hours for ASTM testing = $684,000 savings for 6 months
- 32. 32 Ethanol Percentage Have the predictive capability to determine octane in blends with up to 10% EtOH Recently added equation for octane determination in blends with 10% EtOH and greater During trial of XDS could determine EtOH percentages from 0 – 15%
- 34. 34 Diesel & Biodiesel Oversight NIR vs cetane motor to predict cetane number Biodiesel Equations Based on 6 refineries of base diesel & 3 sources of B100 0-5% curve to determine if any biodiesel is present – good to ~0.2% 5-25% curve to quantify percentage of biodiesel
- 35. 35 Fatty Acid Methyl Esters (FAME)
- 36. 36 Other Work Useful in predicting oxygenates in reformulated gasoline when MTBE was used in blending Distillation data – Prefer simulated distillation analyzers since analysis is ~13 mins Presently working to predict various properties in jet fuel & kerosene
- 37. 37 Conclusions NIR has been a very useful tool in octane predictions for a number of years Big push for smaller refineries in system to adapt to on-line blending techniques As more & more mandates are pushed through, NIR providing to be reliable in prediction of ecofuels
- 38. 38
- 39. Online NIR Applications in Polymer Industry John Martin Product Specialist Metrohm USA
- 40. Online NIR - Advantages • Fast • Process Optimization • End Point of Reaction monitoring • Reduce Over-Processing of Product • Improve Measurement Precision • Improve Production Consistency • Multiple Analyses • Safety and Environmental
- 41. Real Time Monitoring using NIR Sampling : Optical Consistency Probes: Contact or Contactless Immersion Probe: 0-15% dissolved solids Reflectance Probe: over 15% Solids Fiber Optics: Connects Monochromator to Probe • Fiber Material: Ultra Low Moisture Quartz • Fiber Counts: Illuminators/Collectors • 1/1, 74/74, 210/210 • Fiber Diameters: • Single Fibers 600 Microns • Bundles 200 Microns • Fiber Length: 2 to 250 Meters
- 42. Sampling systems: Benefits • Laminar rather than turbulent flow • No bubbles • Filtering possible (particulate/water) • Temperature control (better accuracy) • Accessibility to probe (for cleaning, running calibration standards) • Ability to collect the sample at the same point where the NIR collects the spectra (for calibration / validation /updating of the models)
- 44. NIR - Polyols & Polyurethane Matrix Analytes Oxide-based Polyols OH# Primary OH# Secondary OH# EO/PO Ratio Residual Oxides Moisture Ester-based Polyols Acid Value OH# Substituted Polyols Primary Amines Secondary Amines Polyurethanes Isocyanate Levels OH#
- 45. Polyol Process- Sampling Conditions • 240C • Nitrogen sparging (bubbles) • Undissolved solids (turbid) • Turbulent mixing
- 46. Reactor Spectra 1100 1400 1700 2000 2300 Wavelength (nm ) 0 0.5 1 1.5 2 2.5 Absorbance(log1/T) Polyol Spectra
- 47. Overtone Region 1300 1350 1400 1450 1500 1550 1600 Wavelength (nm) 0 0.1 0.2 0.3 -0.1 -0.2 -0.3 SecondDerivative(log1/T) Acid Value Moisture Hydroxyl Number Polyol Spectra – Overtone Region
- 48. Combination Band Region 1820 1870 1920 1970 2020 2070 Wavelength (nm) 0 0.2 -0.2 SecondDerivative(log1/T) Acid Value Moisture Hydroxyl Number Polyol Spectra –Combination Band
- 49. Calibration Results: Hydroxyl Number - . . . . . .,,,,.,, .,. . . . . . . 10 20 30 40 50 60 Laboratory Results (OH #) 10 20 30 40 50 60 Near-InfraredPredictedResults(OH#) Calibration Results: 2030 nm SEC = 0.41 R2 = 0.99 Validation Results: SEP = 0.38 R2 = 0.99
- 50. Calibration Results: Acid Value . . . . . . .......... . . . . . . . . 0 10 20 30 40 50 Laboratory Results (Acid Value) 0 10 20 30 40 50 Near-InfraredPredictedResults(A.V.) Validation Results: SEP = 0.26 R = 0.99 Calibration Results: 1900 nm SEC = 0.28 R = 0.99
- 51. NIR Process Control Chart * * ** ** ****** * ** ***************************************** - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 0 2 4 6 8 TIME (HOURS) 0 10 20 30 40 50 60 70 HYDROXYL/ACIDVALUES ACID VALUE HYDROXYL # - *
- 52. NIR Process Control Charts - - -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- - - ----------------------------- - ---- - --- ------------------- - ----------------- - --------------------------------------------- -------------------------- --------------------------------------- -------------------------------------------------------- 0 5 10 15 20 Time (hours) 100 150 200 250 300 HydroxylNumber 0 0.1 0.2 WaterContent(%) Moisture Hydroxyl # - -
- 53. Real-Time Monitoring of Polyurethane Batch Production 5000 Liter Batch Reactor Temperature 55 ⁰ C Immersion Probe: 300⁰C & 5000psi Pathlength 1cm (5mm gap)
- 54. Raw Material Spectra 1100 1300 1500 1700 1900 2100 2300 2500 Wavelength (nm) 0 0.5 1 1.5 2 2.5 log(1/T) Isocyanate Polyol Polyurethane
- 57. Isocyanate Calibration 2 4 6 8 10 Titrimetric Isocyanate (%) 2 4 6 8 10 NIRIsocyanate(%) Calibration: R2 =0.98 at 1648 nm, SEC=0.25% Validation: R2 =0.97, SEC=0.23%
- 58. 0 25 50 75 100 125 150 175 200 225 250 275 Time(minutes) 0 0.1 0.2 0 2 4 6 8 10 Change Range Batch Control charts AverageChange(%) Range(%)
- 59. Primary and Routine Methods • Hydroxyl Number • Acid Value • Isocyanate • Moisture Primary Method -Titration Routine Method - NIR Single Measurement All Parameters
- 60. Real-Time Monitoring of a Polyester Batch Reactor • Acid Value • Trend Analysis • End Point Determination • Nitrogen Purged • Two Stage Agitation • Ambient Pressure • High Temperature • Multiple Products
- 61. Bundle Fiber: With and Without Agitation 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 0.7150 1.1132 1.5114 1.9096 2.3077 2.7059 3.1041 3.5023 3.9005 4.2987 Wavelength Absorbance
- 62. Samples Spectra 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 0.7276 1.2441 1.7605 2.2770 2.7934 3.3099 3.8263 4.3428 4.8592 5.3757 Wavelength Absorbance 1306 1340 1374 1408 1442 1476 1510 1544 1578 1612 1646 1680 1714 1748 1782 1816 1850 1884 1918 1952 1986 -0.8976 -0.7273 -0.5570 -0.3867 -0.2164 -0.0461 0.1242 0.2945 0.4648 0.6350 Wavelength Intensity
- 63. Calibration & Validation: Acid value 14.0 21.5 29.0 36.5 44.0 51.5 59.0 66.5 74.0 14.0 18.0 22.0 26.0 30.0 34.0 38.0 42.0 46.0 50.0 54.0 58.0 62.0 66.0 70.0 74.0 Calibration Set : Calculated vs Lab Data • PLS, 2 Factor • 1376-1472nm • and 1878-1936nm • R2 = 0.9945 • SEC = 1.18 15.0 19.8 24.6 29.4 34.2 39.0 43.8 48.6 53.4 58.2 63.0 15.0 18.0 21.0 24.0 27.0 30.0 33.0 36.0 39.0 42.0 45.0 48.0 51.0 54.0 57.0 60.0 63.0 Validation Set : Calculated vs Lab Data R2 = 0.9830 SEP = 1.94 Compared to a Titration with a Visible Endpoint
- 65. Summary • Choose the right analytical technique from wet chemistry to spectroscopy suitable for your process applications. • Sampling systems to improve sample presentation and measurement • Stable NIR instrumentation improves method ruggedness & calibration stability • Selection of correct probe and fiber count improves the accuracy and precision for process NIR applications.
- 66. Thank You
Editor's Notes
- Greetings, as Scott mentioned, I am the product manager for the Applikon group here at Metrohm. I’m going to be leading off the webinar today with an introduction to process analysis and setting the stage for the other parts of the webinar.
- Here is a very brief overview of my part of the webinar. We’ll start with a quick introduction to the Metrohm Applikon group, followed by an overview of process analysis. From there I will introduce you to our process analyzers, and will then end with some example applications which have been performed on our analyzers.
- The global headquarters from Applikon are in Schiedam, Netherlands, and this is where development, production and global distribution take place. The North American operations are based outside of Tampa, Florida, in Riverview, FL. Here we perform the custom assembly of analyzers, perform custom application development, and provide technical support. We also have a field office in Houston, TX where demonstrations and training take place, along with applications and technical support. To support sales and service in Canada we have an office in Toronto, Ontario.
- Process analysis is just what it implies, some type of analysis that is done on the process. The type of analysis can vary widely depending on the process being monitored. It may be a simple measurement of temperature, pressure, or maybe pH. The analysis needs may be more complicated, and this is when an analyzer or analysis method is needed. Generally the closer to the process you can get the faster the analysis turnaround will be. Offline analysis involves a sample being collected, taken to a laboratory, and then analyzed, with the results being communicated back to the process area. This can be a rather involved and time consuming process, so it is better to move the analysis much closer to the process. By taking the analysis to the process or manufacturing area there are three potential analysis approaches. You can remove a sample and then analyze it right at the process line, this is an at-line analysis. You can have a bypass loop where the stream to be analyzed is directed, this is considered an on-line analysis. And finally you can do the analysis directly on the process line, an in-line analysis. An important benefit of using process analysis is safety. You decrease the number and frequency of exposing personnel to potentially hazardous areas and samples, reduce trip/fall chances and loss of sample, and improve sample integrity.
- As I mentioned in the previous slide taking a sample from the process and then taking it to a laboratory for analysis can be time consuming. As shown on the left side of this slide the entire sampling and response cycle can take an average of 30 to 60 minutes. By implementing an analysis that is at-line, on-line, or in-line the response cycle can be shortened significantly to only a few minutes, even shorter than that depending on the type of analysis being performed. This reduced response time means more effective monitoring of a process, leading to safer operating conditions, cost savings, and a better product. Also it means that the process is more closely monitored so that unwanted or unsafe conditions can be avoided, or if there is a problem it can be detected more quickly.
- Analysis done in a laboratory environment is quite different than that done in a process or production environment. On this slide I wanted to compare and contrast the two analysis approaches. As I previously mentioned there is a time gain by moving the analysis from the lab to production, but there are other trade-offs to be aware of. The two pictures shown on this slide highlight that the environment where the analysis is happening is quite different. In the lab you typically have more advanced analytical technology which can deliver a very low standard deviation on the results. The instruments in a lab also require regular professional maintenance. A process analysis typically is done using simpler technology, which delivers results with a slightly higher standard deviation. Due to the rugged environments commonly found in a production environment a process analyzer must be able to perform well in less than ideal conditions. This means that the analyzer should not require constant or delicate adjustment, and should work well with regular maintenance only every 6 to 12 months, other than replacing any needed reagents.
- Earlier I mentioned earlier there are some relatively easy measurements, such as temperature or pressure. Process analysis can require a wide array of much more advanced analysis methods. Some of the most common techniques that we offer are shown here, and include various kinds of titrations, ion-selective electrodes, colorimetry, and of course near-infrared, which you will be hearing a lot about later on. This array of methods mean that you have analysis options that span the range of percentage of a species in a sample down to ppt – parts per trillion.
- To serve the need for a wide array of samples and sampling situations we have an equally wide range of analyzers and configurations. What I am showing here is the range of Applikon Process Analyzers, later in the talk you will have the opportunity to see the near infrared analyzer options. As you can see we offer online and atline analyzers along with fully integrated solutions, including enclosures and sample pre-conditioning.
- Let’s transition now and talk about a couple of example process applications. I want to start off by showing a diagram of a potential crude oil processing layout. I have indicated just a few points where a process analyzer might come into play. Later in the talk you will hear more about where near infrared methods can fit into this picture. Also I have indicated a few relevant types of analysis that can be performed by a process analyzer. Some of these applications include analysis of water in crude oil – performed by a Karl Fisher titration, total acid number or salt in oil. The last one, analysis of ammonia and hydrogen sulfide in sour water stripping we will talk more about on the next two slides.
- For those who may not know, let’s first define what sour water is, and then what sour water stripping is. Sour water is water that contains sulfur and ammonia. Sour water is typically formed when crude oil, or natural gas, containing hydrogen sulfide is treated with an amine solution with the resulting products dissolved in the water. To reuse the sour water the sulfides and ammonia need to be removed, or stripped, from the solution, using steam. Effective monitoring of the sour water stripping process means improved removal of the sulfides and reduction the amount of steam used in the process, and that means cost and time savings.
- On the right hand side we have a picture of an analyzer that has been configured for the analysis of a sour water stripping process. The typical configuration has the left side set up for the analysis of ammonia, in the 5 to 100 part per million range, and the right side is configured for the analysis of hydrogen sulfide in the zero to 50 part per million range. The sulfide concentration is determined by a precipitation titration using silver nitrate and monitoring the concentration of silver in the solution to determine when all the sulfide has been reacted. The ammonia concentration is determined by a dynamic standard addition titration, this is where a standard amount of ammonia is added to the sample before titration to aid in the determination of the initial ammonia concentration. In a dynamic standard addition titration the amount of ammonia added is determined by a method that is applied during the titration, adds leads to an adaptable measurement range and a reduction in reagents used.
- In our second example we’ll talk about the benefits of good process control. For this example we’ll look at a caustic scrubbing process. The process analysis monitors the sodium and potassium in the scrubbing solution. Effective monitoring leads to better control of the concentration of caustic in the solution, improving the scrubbing conditions, which leads to improved process control and cost savings. If there is an upset in the process it can lead to a loss of up to $100,000 in raw materials, off spec product that can’t be sold, and reprocessing of the product. In the plant where this was applied they typically had 4 upsets per year. Compare those losses to the cost of an analyzer and sample conditioning system and the analyzer proves it value very quickly.
- We’ll start by looking at the distributed control system results using flow control for the caustic scrubber. As you can see the caustic concentration can vary from almost 0% up past 8%. The average caustic concentration is 4.81% with a standard deviation of 1.61. Let’s see what happens when our process analyzer is brought on-line.
- And here we see the effect of using a process analyzer to monitor the caustic concentration. Please note that I have tried to match the y-axis, representing percent caustic, as closely as possible to the previous slide, so that you can see the real impact. Now the variance has been limited to a range between 1.5 and 4%, with one spike to about 5%. The average caustic concentration has been lowered to 2.97%, with a much lower standard deviation of 0.43. Overall the system is kept much more stable and maintained.
- The optimization of the scrubbing process has many benefits – improved quality, speed, less variance, improved safety, less waste, and of course saves money. To recap the caustic concentration was dropped from 4.8 to 2.9%, and since then the average has been dropped to 2%. The process improvement has led to savings of approximately $1000 a day, or $200,000 a year, since the process is run on a 4 day on, 2 day off schedule.
- In conclusion I just want to summarize what we have talked about. We started with an overview of process analysis, talked about the customizable analyzers from Applikon along with their range of diverse applications, and we talked about 2 different examples showing the benefit of good process control. I’d like to thank you for your attention during my part of the webinar, and from here I want to hand the presentation over to the near-infrared applications part of the webinar. Up next is Lucy Thurston, from Marathon Petroleum Company who will talk about their process NIR systems and applications.
- Process Optimization: Continuous monitoring and updating results on the order of minutes rather than hours allows determination of when the endpoint of the reaction has occurred. In many occasions, this allows the batch to be finished several hours quicker than normal, allowing the reactor to be emptied, and a new batch begun more rapidly than previously obtainable. More batches can be produced per day, increasing the amount of product manufactured without requiring investment in more batch reactors. Multiple Analyses: Instead of performing an analysis for each component, NIR can provide multiple analyses with a single NIR scan. In the analysis of polyols for example, multiple determinations (OH#, Acid Value) can be obtained simultaneously. For manufacturing sites producing a number of different polyols and polyurethanes, a single NIR instrument can produce results for all products. Safety and Environmental: Analyses are performed without the need for chemicals or solvents, reducing costs for purchase of these materials, and the costs for their disposal. In the case of on-line measurements, no sample collection is required, so safety risks in collection procedures is eliminated.
- Sampling: optical consistency is important. Variation in Total solids, pathlength and penetration depth can alter the spectral quality As Scattering Increases, Fiber Count Increases to Maintain Accuracy & Precision. As Fiber Count Increase, Fiber Length Decreases to Maintain Costs
- Rugged Optical Bench least sensitive to Vibrations Suitable for harsh environmental conditions
- NIR has been successfully implemented in many areas of polyol and polyurethane production. NIR has been used both in laboratory systems, and directly interfaced to the manufacturing process. Determination of whether a laboratory or a process NIR is optimal depends on many factors, including the number of products made, the number of reactors on a site, and distances between reactors. Also important in this determination is whether the analysis results will be used for process control or for Quality Assurance.
- Pathlength = 2 (Gap), 1 to 20 mm 316 Stainless Steel Construction: up to 300C at 5000 psi (350 Kg/cm2) Length by Diameter:12 inches (30.5cm) , 1 inch (2.54 cm) Various Metal to Sapphire Seals
- The Red curve indicates average change charts and process trends. The Range – Blue curve indicates the short term variation. Spike in the range chart indicates when chain-extender was added. For the urethane batch reaction the average change chart demonstrates the extent of reaction for each of reaction steps. The slowing of the first reaction is followed by an acceleration after the addition of the chain extender. It is noteworthy that the 60 min (from 100 to 160 min) plateau is the average change chart before the chain extender was added by using pre-determined time-dependent reaction end point.