Amide

Presently, amide mixer and reactor samples from MM7, MM8 and MAA2 are analyzed offline via high performance liquid chromatography (HPLC). Values obtained from this analysis include conversion index (CI), water balance and decomposition index (DI).

Recent requirements for amide reactors have created a need for a continuous optimization, which will ensure that reactors run at consistently high efficiencies and give the maximum possible output.

There are several drawbacks that limit the analysis for optimization purposes:

  • Frequency of data collection – Currently 1-2 samples are collected and analyzed every 24 hour period per reactor. This low rate of data collection severely limits the rate with which the reactors can be optimized and is insufficient.
  • Offline sampling – There are difficulties in the handling of amide mixture samples. Furthermore, the sample composition is prone to change post-sampling.
  • Delay – It will often be a matter of hours before HPLC data are fed back for response of the technical team.
  • Reliability – Being high in sulfuric acid and sensitive to moisture, the nature of the samples makes them difficult to analyze by HPLC.
  • Spectral processing facilitates use of a variety of chemometric based routines for multiple property measurements.

AI-60 in Amide Reactor Optimization

Proton NMR Spectroscopy technique offers numerous advantages:

  • A major advantage of 1H NMR is that individual scans only require a matter of seconds to collect. Several scans may be required to obtain a reliable, composite spectra.
  • Online analysis – 1H NMR does not need to come into physical contact with samples and therefore lends itself to being adapted to online analysis. Thus all the problems associated with physical sampling can be circumvented.
  • Reliability – 1H NMR is inherently quantitative as it measures the relative signals from hydrogen nuclei (protons) present on each of the molecules in the mix, given that complete relaxation of these nuclei is allowed to occur. The relative ratios of components can therefore be accurately determined from first principles, without the need for continuous calibration.

Results and Discussion (I)

When reviewing the 1H NMR spectrum of MAM in DMSO-d6, a large singlet is observed at 1.60 ppm with an integral of 3, this signal originates from the 3 methyl protons. Two signals occurring at 5.11 and 5.46 ppm respectively and each integrating for a single proton are assigned to the 2 methylene protons. A broad doublet is observed at 6.81 ppm and is assigned to the 2 amide protons.

Reference Spectra of MAM

Results and Discussion (II)

When reviewing the 1H NMR spectrum of MAM in H2SO4, the HsSO4 contributes a large singlet peak at 11.22 ppm but otherwise many of the same features are present. All signals have shifted downfield, the methyl singlet to 2.20 ppm, the two methylene singlets to 6.29 and 6.62 ppm respectively and the broad amide singlet to 8.20 ppm.

Reference Spectra of MAM

Results and Discussion (III)

When reviewing the 1H NMR spectrum of MAA in DMSO-d6, a large singlet is observed at 1.26 ppm with an integral of 3, this signal originates from the 3 methyl protons. Two signals occurring at 4.98 and 5.56 ppm respectively and each integrating for a single proton are assigned to the 2 methylene protons. The singlet at 12.10 ppm is assigned to the carboxylic acid proton.

Reference Spectra of MAM

Results and Discussion (IV)

When reviewing the 1H NMR spectrum of MAA in H2SO4, many of the same features are present. Similar to MAM, all signals have shifted downfield, the methyl singlet to 2.13 ppm, the two methylene singlets to 6.45 and 6.89 ppm respectively and the carboxylic acid singlet has presumably disappeared beneath the H2SO4 peak.

Reference Spectra of MAM

Results and Discussion (V)

When reviewing the 1H NMR spectrum of SIBAM in DMSO-d6, a large singlet is observed at 0.94 ppm; this signal is assigned to the 6 chemically equivalent methyl protons with an integral of 6. A broad singlet is present at 9.56 ppm, which is tentatively assigned to the 2 amide protons but the integral is difficult to determine due to the broad peak shape.

Reference Spectra of MAM

Results and Discussion (VI)

When reviewing the 1H NMR spectrum of SIBAM in H2SO4, the methyl singlet appears to have been shifted downfield and now occurs at 1.94 ppm. Finally, the broad amide doublet can be seen at 8.86 ppm but is poorly resolved from the large, sulfuric acid singlet. Tal, both up field ‘singlets’ could be from the methyl groups, the solvent could have induced some in equivalence.

Reference Spectra of MAM

The Challenge

  • Organic silicon can be used to produce a great variety of products. The main product is chloro(methyl)silane.
  • Raw materials, such as silica powder, chloromethane, chlorobenzene, etc., are synthesized by catalytic reaction into methyl chlorosilane, phenyltrichlorosilane and other monomers, then generate various polymers through a series of chemical reactions and then are processed into different kinds of products.
  • A series of products derived from methyl chlorosilane monomers usually account for more than 90% of total organic silicon products.
  • Production technologies of organic silicon are very complex and its industrialization is challenging.
  • Moreover, any change needed to correct an out of spec product required almost twelve hours.

AI-60 in Organic Silicon Production

The System is integrated with the plant data base system, transmitting online process data to the control room for process control.

4IR Process AI-60 Predictions (with the Models from MGW):

  • M1 Methyltrichlorosilane
  • M2 Dimethyldichlorosilane
  • M3 Chlorotrimethylsilane
  • MH Methyldichlorosilane
  • The AI-60’s permanent magnet design requires no cryogens as the system is powered by one standard wall outlet.
  • Samples can be run neat, in conventional protonated solvents, or, in deuterated NMR solvents. Deuterated solvents are not required.
  • Probes accept standard 5mm or 10mm NMR tubes.
  • Flow cell options are also available.
  • Complete automation of all NMR tasks (Acquisition, Processing, Integration, Results Reporting) enables walk-up applications with minimum user training and/or experience.
  • Spectral processing facilitates use of a variety of chemometric based routines for multiple property measurements.
  • The use of an NMR as a simple flow detector for benchtop reaction monitoring, mixing monitoring, dilution monitoring, or conversion monitoring has been limited by the need to bring the ‘reaction’ to the typical ‘supercon’ NMR lab.
  • With the AI-60 system continuous flow NMR can be performed “at the bench.” The system uses a high resolution permanent magnet with a simple flow cell and total system volumes of 2 to 5 ml depending on the length and diameter of the transfer tubing.
  • Further, detection limits of analyses in the 200+ ppm range are possible without the use of typical deuterated NMR solvents. Analysis times of 5 to 20 seconds are achieved at flow rates of 1 to 20+ ml/minute.

Case Study – Continuous Flow Reaction Monitoring

  • AI 60 NMR Analyzer was used to monitor three reaction process, Imine formation, CDI mediated, amide coupling and transesterification.
  • Each reaction was monitored at regular intervals by both 400 and 60MHz NMR and the data was overlaid to compare the profiles obtained at the two different field strengths.
  • NMR data generated from these three reactions demonstrates the application of low field NMR as a PAT tool for reaction monitoring.

Case Study – Reaction Monitoring

  • In the figure to the immediate right the expansion plots corresponding to the CH3 region of the 1H NMR spectrum are shown. Peak assignments corresponding to the constituents of the reaction are given. Excellent resolution of each CH3 resonance from each constituent in the reaction (t-butyl alcohol, acetic anhydride, t- butyl methyl ester, and, acetic acid) are well resolved.
  • Integration of each peak allows quantitative determination of each constituent’s concentration over the course of the entire reaction. As shown in the lower right figure, reaction kinetics can be determined. The Lab AI-60 system’s small footprint, high sensitivity, excellent resolution, and automated processing routines enable new opportunities and applications for reaction monitoring by NMR.

Case Study – Reaction Monitoring

The Challenge

  • An oil offshore platform is a large structure with facilities to extract and process oil. The fluids produced from offshore wells need treatment before they can be shipped ashore via pipeline or tanker. Hence the process plant is a vital part of any offshore installation.
  • The processes in offshore treatment plants are generally rather simple compared to those seen in refineries and chemical plants.
  • The fluids produced from offshore oil wells are a mix of formation water and hydrocarbons in different molecular configurations. Therefore the majority of the water, dissolved gasses as well as NGLs have to be removed from the crude oil before the oil leaves the platform.
  • The challenge of offshore plants, is continuous real time monitoring of the fluids which can be exclusively done by the AI-60 Process analyzer.

Offshore Production Operations and Parameters

Control Parameters:

  • TBP (True Boiling Point) yield
  • Aromatic content
  • Specific Gravity
  • Olefin content%
  • Pour point%
  • Sulfur%
  • Water%

The Challenge

  • In a base oil production plant the operating conditions needed to produce products at a desired specification are very sensitive to feed quality.
  • Base oils are produced from Vacuum Gas Oils (HVGOs) and Deasphalted Oils (DAOs) which having different viscosity by aromatic solvent and solvent dewaxing. Feedstock having the same viscosity may have a very different composition depending on the crude origin and therefore these have to be refined by using different operating conditions in order to produce base oils at desired specification.
  • A way of defining the feed composition is to determine its hydrocarbon distribution (i.e. aromatic, naphthenic and paraffinic carbon content); this can be obtained off-line (ASTM D2140), or by laboratory high resolution 13C NMR.
  • Unfortunately the feedstock characterization can be only available one or two times a day and it is not actually possible to follow any fluctuation in quality.
  • Moreover, any change needed to correct an out of spec product required almost five hours.

The Challenge

The strategic importance of placing a Process NMR system at the pipeline point of entry.

To verify that the pipeline tender is fully intact and meets delivery specifications.

To remove liability of the supplier should the product experience changes/contamination during pipeline transmission to the end user.

Placement at the point of entry also provides the supplier with an early warning regarding:

  • Stratification of product while in delivery storage tanks prior to shipment.
  • Detrimental co-mingling with tank heels from previous tender
  • Asphaltene precipitation (plugging)
  • Water Separation which typically causes corrosion.

This way early corrective actions can be taken (i.e. diverting tender to upstream holding tankage) so that improper delivery to the end user is avoided. Once the improper material is segregated, corrective measures can be applied (i.e. blending to specification, re-processing, etc.).

Criteria

Quality/Value Measures are typically set by the end user.

  • The unique properties of crude oil are defined by the Distillation Cut Point. Undesirable mixing between different types of crude will be immediately detected. The fluidity of the crude oil is mainly defined by its density as the pipelines have restrictions on the level of crude density.
  • Sulfur concentration is one of the major parameters of the crude product and indicates the propriety of the stream. Chlorine (including organic chloride) is often present in crude oil, and its concentration can vary greatly depending on the origin.
  • Performance Measures are used to ensure the proper operation of pipelines and process units. TAN is a measurement that is critical in defining the corrosive capacity of the crude oil for protecting the integrity of the pipeline. Asphaltenes are critical in defining and avoiding pipeline plugging and fouling.
  • Integrity Measures are used to ensure storage tank and pipeline tender integrity, as TAN, Asphaltenes and Water are immediate indicators of improper co-mingling (crude incompatibility and/or tank heel mixing) and tank stratification.

Critical properties

The properties measured fall into three categories:

Quality/Value Measures:

  • Distillation Cut Point: IBP; Cumulative Yields at 150°c, 220°c, 350°c, 520°c
  • Density at 60°c
  • Simulated Distillation: T10, T30, T50, T70, T90
  • % Sulfur
  • Chlorine (including organic chloride)
  • Mercaptans
  • Hydrogen sulfide

Performance Measures:

  • TAN (Total Acidity Number)
  • Asphaltenes
  • Water

Integrity Measures:

  • TAN (Total Acidity Number)
  • Asphaltenes
  • Water

System Configurations

Difference in the Composition of Crude Oil

Sample Preparation System

Sampling from the pipes and switching between different process streams

Temperature control; Flow control; Pressure control

Recovery System

Measurements Required Accuracy

Crude Oil measuring conditions:

Temperature – 10 to 30
Pressure – 0 to 0.5 Mpa
Density – 800 to 900 Kg/m3

The Challenge

  • Switching to heavy crude oils increases the production of heavy distillates further cracked by the FCCU (Fluid Catalyst Cracking Unit) into lighter fractions.
  • FCCU process optimization is a complicated task involving many variable process parameters.
  • FCCU process optimization software adjusts operation parameters based on a static database. Physical properties of the feedstock fluctuate constantly turning the FCCU into a highly dynamic system.
  • Real time on-line monitoring is critical in the optimization of the FCCU process. 4IR’s AI-60 Analyzer provides game-changing technology which enables real time analytical data.

FCC Profits – Process Sketch

FCC Profits – Process Sketch

Increase FCC Profits – Saving with AI-60

Utilizing the AI-60 in FCCU helps to optimize the process in the following ways:

  • Bring valuable information online.
  • Provide ability to run combined feedstock (both from the VAC unit and Tankage).
  • Increase unit feed variability.
  • Shift closer to constraint of process unit.
  • Minimize lab sampling.
  • Feed forward control into FCCU Advanced Process Control software.

The Challenge

Optimization of the Crude Distillation Unit process conditions is the main challenge for every refinery.

Continuous real time monitoring of the feedstock and outgoing istillates is a minimal requirement for ensuring:

  • Minimal influence on the production capacity of each distillate due to changes in crude oil.
  • Maximum production of high value distillates at the expense of heavier distillates of lower value.
  • Stability of the CDU operation conditions.
  • The prevention of production of off-spec material.

Economic Drivers

The problem is maximizing the production of the distillates according to the following properties:

  • Distillation points: IBP;T10;T50;T95;IBP
  • Cloud point
  • Freeze point

Naphtha is worth more than Jet Fuel
Jet Fuel is worth more than Diesel Oil
Diesel Oil is worth more than Fuel Oil
Fuel Oil is worth more than Residual

  • Using Diesel as an example: any distillate with a T95 higher than 315ºc should not get into the Diesel cut. This distillate adversely affects the combustion and cold properties of Diesel Fuel.
  • Since refineries are unable to measure properties such as T95 quickly and accurately enough, they set their operational targets at a safety margin of 10% below 315ºc. This means that they are ‘giving up’ on a fractional difference of the lower margin (less valuable) atmospheric Fuel Oil cut.
  • As a result: 10% of Fuel Oil is lost to Residual; 10% of Diesel Oil is lost to Fuel Oil; 10% of Jet Fuel is lost to Diesel Oil and 10% of Naphtha is lost to Jet Fuel
  • Therefore, based on a refinery’s operations, (temperatures, draw rates, crude feed rates, etc.) and with an appropriate analyzer (such as the AI-60 Petroleum Analyzer) the refinery can push its control limits higher and get extra high end product.
  • Based on a conservative calculation, the Aspect AI-60 enables the refinery to reduce the safety margin to 5% (instead of 10% that currently is being used).

Basic Configuration

Control Parameters

The product streams are monitored continuously during the production and are analyzed by the AI-60 Analyzer.

Data collected from each stream will predict the following physical parameters of the CDU incoming crude and distillates products:

Crude Oil

  • API
  • Distillation
  • Conradson Carbon
  • Water in Crude
  • Ashphaltenes
  • Sulfur

HGO

  • Density
  • Distillation
  • Pour Point
  • Cloud Point
  • Viscosity Index
  • Pariffins
  • Naphtenes
  • Aromatics

LGO

  • Density
  • Distillation
  • Pour Point
  • Cloud Point
  • Viscosity Index
  • Pariffins
  • Naphtenes
  • Aromatics

Diesel Oil

  • Density
  • Distillation
  • Cetane Index
  • Cloud Point
  • Pour Point
  • Viscosity Index
  • CFPP

Kerosene

  • API
  • Distillation
  • Cetane Index
  • Flash Point
  • Freeze Point
  • Aromatics
  • Naphtenes

Naphtha

  • Density
  • Distillation
  • PONA
  • Octane

Case Study Yanshan Refinery

The Challenge

The worldwide refining industry has undergone a major transformation in the last decade due to changes in regulatory and market forces. At present, refineries must be flexible enough to respond immediately to crude oil changes and deviations in product demands.

The required flexibility can only be achieved by stringent monitoring of the quality of the incoming crude and the outgoing product. For low cost and compatible feedstock refineries need to apply a rigorous Crude Blending process.

Effect of Variation in Crude Feed Quality

  • Unit operating conditions are upset by changing feed composition.
  • Product slate is not maintained at optimum.
  • User incurs significant financial penalties.
  • Modern refinery is a low-margin enterprise, typically less than 5 % after fixed and variable costs. Refinery feedstock costs around 80% – 90% of the cash flow.
  • In the past refineries were based on the distillation of conventional crude oils.
  • Current economics and variations in the price of crude oil force refineries to reduce their cost of feedstock by blending high value light crude oil with heavy crude oil of inferior quality.

Basic Configuration

Control Parameters

True Boiling Point

Dairy Products

Food Oil Analysis

Oils and fats are key elements in the taste, melting properties, shelf life, and appearance of foods.
Composite Spectra of 4 Oils (Olive, Sesame, Canola and Caesar Salad Dressing) Showing Differences Observed by NMR.

Liquors Analysis

Baby Food Analysis

Overlay of all 5 baby food products showing NMR variability

Bio-Technology Applications

In situ NMR analyses of Escherichia coli fermentations

  • Analyses of the ‘mixed acid’ fermentation during grow mb-0th of Escherichia coli on glucose and citrate will be performed to identify and quantitatively estimate the concentrations of the two substrates.
  • Identification of fermentation substrates and products can be achieved by coincidence of selected diagnostic proton signals of individual compounds in the same solvent.
  • The entire course of these in situ proton measurements during grow mb-0th can be obtained automatically. The utilization and formation of the substances in the fermentation is being monitoring simultaneously, without sampling and individual analysis.
  • This versatile and rapid method for the simultaneous, direct and automatic analysis of mixtures of many compounds has the potential to be extended to routine on-line analyses of industrial fermentations.

Quantifying Omega 3 In Fish Oil Production

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