Title of Invention

"ON-LINE CONTROL OF A CHEMICAL PROCESS PLANT"

Abstract

A process plant for the manufacture of halobutyl rubber is provided with online monitoring and control of the process parameters to control the properties of the product. It incorporates an in-situ measurement system that does not require the removal of any sample material from the process. It uses a Fourier Transform Near Infrared (FTNIR) spectrometer, fiber-optic cables, a viscometer for measuring solution viscosity and a Resistance Temperature Device ( RTD) for temperature measurement. An online real-time analyzer system using a Constrained Principal Spectral Analysis program predicts the property of the polymer product and provides the process control system with analysis of the data using derived relationships between the physical properties of the polymer and these spectral measurements and the measured values of fluid viscosity and temperature. Differences between the predicted and desired property of the product are used to control process parameters. The method can be used for a variety of chemical process plants.

Full Text

PATENT APPLICATION TITLE: ON-LINE CONTROL OF A CHEMICAL PROCESS PLANT Field of the Invention The invention relates to a chemical plant and to a method of controlling chemici-processes in a chemical plant. More paricularly, the invention relates to a method for ce strolling Mooney viscosity, polymer unsaturation, comonomer incorporation, halogen content, molecular weight and molecular weight distribution during polymerization or halogenation process of isolefin copolymers and mult iOlefins, in particular butyl rubber. Description of the Related Art A promineat method for controlling the polyinerization of olefiris in a medium of inert solvents o;- diluents involves measuring the concentration of polymer ir the medium and the viscosity of the polymer solution in order to calculate a single vanab'e-the "Mooney viscosity " The Mooney viscosity then is used us the shgle variab e by which the entire process is controlled "Single variable" process c ontrol works well where the quality of the desired product is directly proporuonal to only one variable. "Single variable" process control does not work well where two or more variables are directly related to product quality For example, the quality of butyl .-ubber is directly related to both Mooney viscosity and rr.olecular weight distribution within the polymer solution during processing Various attempts have been made to provide additional process control for the production of hu'.yl rubber based on the molecular weight distribution of the polymer as well as on Mo jney viscosity Unfortunately, the methods used to date either have been inefficient or have been hased on insufficiently comprehensive deta to effectively control' the process on the basis of molecular weight as well as Mooney viscosity Accordingly, the present invention relates to a method for online control of a process to produce a product with a property P having a desired value D comprising obtaining a set of measured spectra having measurement errors for a set of calibration samples at least one intermediate step in said process, correcting said measured spectra for said measurement errors to produce a set of corrected spectra for said set of calibration samples, determining a set of weights relating said corrected spectrum of each of said calibration samples to a set of orthonormal basis functions, obtaining a value of said property P for each of said calibration samples, determining a predictive model relating said value for said property P to said set of weights, measuring a spectrum for a test sample at said at least one intermediate step in said process, obtaining a corrected spectrum for said test sample at said at least one intermediate step, determining an estimated value E for said property P for said test sample from said predictive model and said corrected spectrum of said test sample and controlling said process using a calculated difference between said estimated value E and said value D. Accordingly, the present invention also relates to a process plant to produce with a property P having a desired value D comprising a first device for measuring a spectrum contaminated by measurement errors at at least one intermediate step in the process, to give a set of measuredA need exists for an efficient and precise method to control the production of butyl rubber using both Mooney viscosity and polymer molecular weight as process control parameter!; SUMMARY OF THE INVENTION The invention is a method for online control of a process plant having a plurality of steps producing a product with a property' P having a desired value D. It obtains a set of measured spectra for a set of calibration samples at at least one intermediate step in the process and removes the effect of measurement errors for the calibration samples to produce a set of corrected spectra for the set of calibration samples. A set of weights relating the corrected spectrum of each of the calibration samples to a set of eigenspectra are determined, giving a matrix of weights. A. value of the property P of the finished product for each of the calibration samples is obtained. Next, a predictive model relating I he value of the property P of the product for the calibration samples to the set of weights is derived. Next, a spectrum for a test sample at the intermediate step in the process is measured and corrected for measurement errors A value for the property P for the test sample is predicted from a pred .ctive model that uses the set of weights derived from the calibration samples and the corrected spectrum of the test sample. The difference between this; predictec. value and the desired value is used for controlling the process. Optionally, measurements may be made in addition to the spectra and used in the derivation of the predictive model and the predictive process spectra for a set of calibration samples and for a test sample, a second device for measuring said value of said property P for each of said calibration samples and a computer adapted to correct said measured spectra of said calibration samples and said test sample for measurement errors to give a set of corrected spectra, derive a predictive model relating said corrected spectra for said calibration samples to said measured value of said property P for said calibration samples, predict an expected value E for said property P of said test from said predictive model and said corrected spectra for said test sample and control said process.plant using a calculated difference between said expected value E and said desired value D. BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS Figure 1 is a simplified flow diagram of a plant for butyl rubber polymerization. Figure 2 is a simplified flow diagram of a plant for halogenation of butyl rubber. Figure 3 illustrates the equipment used in the instrumentation and control of the process. Figure 4 is a comparison of the measured and predicted Mooney viscosity according to the method of this .nvention. Figure 5 is a comparison of the measured and predicted unsaturation according to the methoc of this invention. DETAILED DESCRIPTION OF THE INVENTION The invention is best understood by reference to the accompanying figures 1- 5 illustrating the preferred embodiment of the invention. The bulk of the world production of butyl rubber is made by a precipitation (slurry) polymerization process in which isobutylene and a minor amount of iscprene are copolymerized using aluminum chloride in methyl chloride diluent at -100 to -90° C. HaiOgenated butyl rubbers are produced commercially by dissolving butyl rubber in a hydrocarbon solvent and contacting the solution with elemental halogens Figure 1 is a simplified flow diagram of the polymerization section of a slurry process. IsobutyUme 101 is dried ;md fractionated in a drying tower 1C3. The water 103a is removed and the fraction consisting of isobjtylene, 2-butenes and high boiling compcnents 103b is purified in the isobutylene purification tower 105. The feud blend drum "09 blends t feed consisting of 25-40% by weight of isobutylene 105b, 0.4 -1 4% by weight of isoprene 107 (depending upor the grade of butyl rubber to be produced) and recycled methyl chloride 111a fron a methyl chloride purification tower 111. Coinitiator solution is produced by passing pure methyl chloride I lib through beds 113 of giaiiular aluminum chilondc ill 30-45 " C. This concentrated solution 113b is diluted with additional methyl chloride and stored in drum 115. The diluted mixture is chilled in catalyst chillers 117 to a temperature of-100 to -90 ° C. The chilled coinitiator 117b is. fed to the reactor 119. The reactor comprises a certral vertical draft tube surrour ded by concentric rows of cooling tubes. The reactor is mixed by an axial flow pump located at the bottom of the draft tube that circulates slurry through the cooling rubes. The copolymernzation reaction is exothermic, rdeaung appro amateiy 0 82 MJ/kg of polymer (350 Btu/lb;. The heat is rerr.oved by exchange to boiling ethyleii'i supplied as liquid to jackets tha: enclose the tube section o:the reactor. The reactor is constructed of alloys that have adequate impact strength at the low temperature of the polymerization reaction As shown in figure 1, the blended feed 109a is chilled by feed chillers 121 and fed into the reactor 119. A branching agent 1.09b may be added to the blended feed 109a to control the properties of the polymur formed in the reactor 119, The output of the reactor 119a consists of butyl rubber, methyl chloride and umeac:ed monomers. Warm hexane and hsxane vapor 125 and a quench agent 125b are added to the reactor outlet line 119a and solution drum X23 and mo:>t of the methyl chloride and unreacted monomers .are vaporized and sent to the recycle gas compressor 151. The butyl rubber solution in liquid hexane is fed to the cement stripper 131 where hot hexane vapor is added 133, The hot cement 131a irom the bottom of the cement stripper 131 contains the polymer in solution in hexane. The hot cement 13la flows through the flash concentrator 137 where cement is concentrated by vaporizing a portion of the hexane in stream 131a The flusned hexane is recycled to the solution drum 123, and the output 137b ofthe flash conceitraloi is tli'S feed for halogc nation, described below with reference to figure 2 All the Methyl chJori.de, monomer;; and a minor amount of hexane 131 b from the cement stripper are recycled 151 is a recycle gas compressor that, in association with dryers 187, methyl chloride purification tower 111. recycle tower 183 and purge tower 185 recycles the methyl chloride111a and isobutylene 185a. Stream 185b is purged from the process In the haiogenation process shown in figun; 2, the butyl rubber solution 137b is stored in tanks 1S3. The solution reacts with chlorine or bromine 155 in one or more highly agitated reaction vessels 157 at 30 - 60 ° C. For safety reasons, chlorine is introcuced as a vipor or in dilute solution because liquid chlorine reacts violently with che butyl rubber solution However, bromine may be used in liquid or vapor form becai.se of its lower reaction rate. The haiogenation by-product of HCI or HBr is neutralized with dilute aqi eous caustic 163 in high intensity mixers 159. Antioxidants and stabilizers 167 such a* calcium stearate and epoxized soybean oil ;ire added The solution is sent to a multi vessel solvent-ierrova) system 171 where steam and water 165 vaporize the solvent and produce crumb like rubber particles in water The final solvent conten1: and the steam usage for solvent removal depends on the conditions in each vessel. Typically, the lead flash drum is operated at 105-120 0 C and 200-300 kPa (2-3 atm) Conditions in the final stripping stage 173 are 101 ° C and 105 kPa (1.04 htm). The hexane 175a is recycled while the halobutyl slurry 173a is sent on for finishing. Figure 3 is an illustration o:f an apparatus useful in the present invention for online monitoring of a flow stream in a manner that enables a prediction of the properties of the finished product. This prediction is, in turn, used to manipulate the inputs and the operating condition* of the equipmeat to obtain finished products with the desired properties. The instrumentation assembly 500 is generally mounted so as to monitor fluids in the flow strearr of the process. As discussed above, in one embodiment of the invention, this is done at the output-to the cement stripper 131 to monitor the cement solution 131 a (figure 1) ar.d to monitor the output 157a after halogenation (fi;»ure 2). At the; output of the cement stripper 131, the measurements are used to determine Moor ey viscosity, Unsaturation and Molecular Weight Distribution while at the output 157a after halogenation (figure 2), the measurements are used to predict the Halot an content af the finished product. Flew into the instrumentation assembly is indicated at 491 while the outflov, is indicatud at 493, with the direction of flew- of the fluids in the process stream as indicated. In the preferred embodiment of the invention, the a: seinbly comprises a spectrometer, a viscometer and a temperature measurement device. In figure 3, the spectrometer is shown at 501. In the preferred embodiment, it is a Fourier Transform Near Infrared (FTNIR) spectrometer. As the name suggests, FTN1R is a spectrometer designed to make measurements in the near infrared region and includes appropriate microprocessors (not shewn) to compute Fourier Transforms of the, input data A fiber optic link 507 from the FTNIR spectrometer sends an infrared signal across the flow stream between the gap 503 -505. The output of the FTNTR spectrometer is spectral cata N detailing the absorption spectia of the fluid being monitored and is used by the process control computer, not shown, as described below a The instruraentation also intrudes a viscometer indicated at 509', that has probe :511 in the fluid flow stream. The probe measures the viscosity product (product of viscosity and density) of the fluid in the flow stream. The viscosity product measurements are output at P for use by the process control computer, not shown. The next component of the instrumentation is a temperature measuring device 511 that comprises a probe 513 that monitors the temperature of the fluid in the flow strearr. The output of the temperature measuring device is a temperature measurement O of the temperature of the fluid in the flow stream. The temperature measurement O is used by the process control computer, not shown, a;j described below In a prefeired embodiment the path length for the infrared signal is approximately 0.8 mm. This path length greatly reduces the need to compensate the absorption spectra for changes in ':he path length compared to conventional methods where the path length is much smaller. The components of the instrumentation the temperature measuring device, viscometer and spectrometer) are not discussed in detail as they woulc. be familiar to those knowledgeable in the art. The outputs N, O and P of the instrumentation assembly arc transmitted to a computer tha: anilyzes the measurements, as discussed below, and predicts properties of the finished pioduct that could be expected from the process. Differences between the predicted and desired propert.es of the produci are used to control the process parameters, also as discussed below. The three measuring instruments disclosed here (spectrometer, viscometer and temperature gauge) are for illustrative purposes only Those knowledgeable in the art would recognize that other measurements could also be made. These additional measurements are intended to be within the scope of the present invention ANALYSIS OF DATA Brown (US 5121337) discloses a method for correcting spectral data for data due to the spectral measurement process itself and estimating unknown property and/or composition data of a sample using such method. This patent is incorporated here by reference and forms the basis for the analysis of the spectral data derived from the FTNIR spectrometer. As disclosed by Brcwn, the first step of the analysis is that of calibration. The spectral data for n calibration samples is quantified at f discrete frequencies to produce a matrix X (of dimensionƒx n) of calibration data The first step ir. the method involves producing a correction matrix Uro of dimensionƒx m comprising m digitized correction spectra at the discrete frequencies/ the correction spectra simulating data arising from the measurement process itself. The next step involves o:thogna.izing X with respect to Um to produce a corrected spectral matrix Xt whose spectra are orthogonal tc all the spectra in U,,, Due to this orthogonality, the spectra in matrix X,. are statistically independent of spectra arising from the measurement process vtself The spectra can be absorption spectra and preferred embodiments described belov: all involve measuring absorption spectra. However, this is to be considered as exemplary and not limiting on the scope of the invention as defined by the appended claims, since the method disclosed herein can be applied to other types of spectra such as rellection spectra and scattering spectra (such as Raman scattering). Although the description giver herein and with reference to the drawings relate to NIR (near-infrared) and MIR (mid-infrared), nevertheless, it will be understood that the method finds applications in-other spectral measurement wavelength ranges including, for example, ultraviolet, visible spectroscopy and Nuclear Magnetic Resonance (NMR) Generally, :he data arising from the measurement process itself are due to two effects The first is due to baseline variations in the spectra. The baseline variations arise from a number of causes such as light source temperature variations during the measurement, reflectance, scattering or absorption by the cell windows, and changes in the temperature (and thus the sensitivity) of the instrument detector. These baseline variations generally exhibit spectral features which are broad (correlate over a -.vide frequency range). The second type of measurement process signal is due to ex-sample chemical compounds present during the measurement process, which give rise to sharper line features in the spectrum. For current applications, this type of correction genergJJy includes absorptions due to water vapor ^aid/or carbon dioxice in the atmosphere in the spectrometer Absorptions due 1o hydroxyl groups in optical fibers could also be treated in this fashion Corrections for contaminants present in the samples can also be made, but generally only in cases where the concentration of the contaminant is sufficiently low as *.o not significantty' dilute the concentrations of the sample components, and where no significant interactions between the contaminant and sample component occurs It is important tc recognize that the.se corrections are for signals that are not due to components in the sample In this context, "sample" refers to that, material upon which property and/or component concentration measurements arc conducted for the purpose of providing data for the model development By"contaminant", we refer to any material which is physically added to the sample after the property/component measurement but before or during the spectral measurement In a preferred way of performing the invention, in addition to matrix X of spectral data being orthogonalixed relative to the correction matrix Ut,, the spectra or columns of Um are all mutually orthogonal The production of the matrix Um having mutually orthogonal spectra or columns can be achieved by first modeling the baseline vana ions by a sc-t of orthogonal frequency [or wavelength) dependent polynomials, which are computer generated simulations of the baseline variations and form the matrix Up, ana then at least one, and usually a plurality, of spectra ofex-sampls chemical compounds (e.g. carbon dioxide and water vapor) which are actual spectra collected on the instrument, are supplied to form the matrix X,. Next the columns of X, are orthogonalzed with respect to Up to form a new matrix X,. The preceding steps remove baseline effects from ex-sample chemical compound corrections. Then, the columns of X, are orthogonahzed with respect to one another to form a new matrix UB, and lastly Up and U, are combined to form the correction miitrix Um, whose columns are the columns of Up and U. arranged side-by-side. It would be possible to change the order of the steps such that the columns of X, are first orthogonali^ed to form i new matrix of vectors and then the (mutually orthogonal) polync.Tiiab forming the matrix Up are orthogonalized relative to these vectors and then combined with them to form the correction matrix Um. However, this changed order is less preferred because it defeats the advantage of generating the polynomials as being orthogonal in the first place, and it will also mix the baseline viiriations in with the spectral variations due to ex-sample chemical compounds and make them less useful as diagnostics of instrument performance. Once the matrix X (dimer.sionƒx n) has been orthogonalized with respect to the correction matrix Um (dimension/x m), the resulting corrected spectral matrix X, will still contain noise data The noise can be removed in the following way. firstly, a singular value decomposition is performed on matrix Xt in the form Xc =U £ V, where U is a matrix of dimension/x n and contains the principal component spectra as colurins, S is a diagonal matrix of dimension n \ n and contains the singular values, and V is a matrix of dimension n . these matrices are multiplied together, the resulting matrix, corresponding with the earlier corrected spectra matrix X, is free of spectral data due to noise For the selsction of the number (k) of priropal components to keep in the model, a variety of statistical tests suggested in the literature could be used but the following steps have been found to give the best results. Generally, the spectral noise level is known from experience with the instrument. From a visual inspection of the eigenspectra (the columns of matrix U resulting from the singular value decomposition), a trained spectros-copist can generally recognize when the signal levels in the eigenspectra are comparable with the noise level, By visual inspection of the eigenspectra, an approximate number of terms, k, to retain can be selected. Models can then te built with, for example, k-2, k-1, k, k+1, k+2 terms in them and the standard errors and PRESS (Predicrive Residual Error Sum of Squares) values ire inspected. The suallest number of terms needed to obtain the desired precision ir. the model or the number of terras that give the minimum PRESS value is then selected The selection of the number of steps i:; made by the spsctroscopist, and is not automated. A Predicted Residual Error Sum of Squares is calculated by applying a predictive model for the estimation of property and/or component values for a test set of samples which were not used in the calibration but for which the true value of the property or component concentration is known. 1 he difference between the estimated anci true values is squared, and summed for all the samples in the test set (the square root of the quotient of the sum of squares and the number of test samples is sometimes calculated to express the PRESS value on a per sample basis) A PRESS value can be calculated using a cross validation procedure in which one 01 more of the calibration sariples are left out of the data matrix during the calibration, and then analyzed with the resultant model, and the procedure is repeated until each sample has been left out once. The method further requires that c properties and or/composii ion data be collected for each of the n calibration samples to form a matrix Y of dimension n x c wheie c > 1. For each of the calibration samples, the corresponding column of Xc is represented by a weighted combination of the principal components (the columns of S). These weight- are called the "scores" and are denoted by s,. A regression relation is then determined between the prc perry (dependent variable) and a combination of the "scores" and other measurements (independent variables). The additional measurements that have been used in the present invention are the viscosity product (product of viscosity and density, denoted by vp) and the temperature t. Once these regression coefficients have been determined, they are used as part of the online prediction process; In the prediction process, the neasured spectra are corrected for background effects as discussed above and the "scores" with the determined principal components calculated. The scores, the measured viscosity product and temperature, and the regression coefficients derived in the calibration process give a prediction of the pi operty undfir consideration It has been found that the Mooney viscosity can be accurately predicted ( within1 unit) using the FTNTR spectral measurements along with the viscosity product and tie temperature. This is a considerable improvement over prior art. The unsaturation consent and halogen content can be accurately predicted by direct correlation with -;he FTNIR spectra. Those versed in the art would recognize that the eigenspectra obtained by this invention through the singular value decomposition form a set of orthonormaJ basis rune-ions for the range of wavelengths used: any member of an orthonormal set of basis functions has a dot product of unity with itsslf and zero with any other member of the orthonornal set of basis functions Other orthonormal basis functions could also be used in the derivation of the predictive model including Legendre polynomials and trigonometric functions (sines and cosines) The use of other orthononrial basis functions is inteided to te within the scope of the present invention Figure 4 shows a comparison of results of prediction of Mooney viscosity from the FTNU;. spectral measurements and the measurements of viscosity product and temperature The abscissa is the measured Mooney viscosity of laboratory samples whie the ordinate is the predicted Mooney viscosity based on the re.gressior relations As can be seen the fit is very good with a standard error of prediction less than one unit Figure 5 is a similar plot comparing the predictions of unsaturation of halobutyl rubber with known values of unsaturation based on spectral measurements only. The abscissa is the measured laboratory value of the halobutyl rubber unsaturation content while :he ordinate is the predicted value of the halobutyl unsaturation from a regression of the spectral value The predictions made as in figures 4 and 5 are accurate enough to be able to provide feedback control of these properties, described below. PROCESS CONTROL The in-situ determination of Moonej viscosity made by the method described above can be used as input to a controller that manipulates the catalysr addition or coinittator rate at 117b in figure . Unsaturation can be controlled by using the in-situ saturation measurement to a controller that manipulates the isoprene content of the feed 107 in figure 1. Comonomer incorporation can be controlled by using the in-situ comcnomer content as the input to a controller that manipulates the butyl reactor feed comcnomer that in a preferred embodiment is isoprene 107. Moleculiu weight distribution can be controlled by using the in-situ molecular weight distribution as the input to a controller that manipulates the quench flow 125b and/or br;mching agent 109b flow to the butyl reactor Butyl reactor helcgen content can be controlled by using the ins-situ halogen content measurement as the input to a controller that manipulates the halogen flow to a butyl halogenation reactor (155 in figure 2). The example given above is for illustrative purposes only. The invention can be used for a wide variety of processes and manufacturing plants. The processes for which the method can be used include, but are not limited to polymerization, steam cracking, olefin purification, aromatic purification, isomerization, catalytic cracking, catalytic reforming, hydrogen ation, oxidation, partial oxidation, dehydration, hydration, nitration, epoxidation, distillation, combustion, alkylation, neutralization, ammination, esterilication, dimerizaion, membrane separation, carbonylation, ketoni/;ation; hydrofonnulation, oligomerization, pyrolysis, solfonation, crystallization, adsorption, extractive distillation, tydrodealkylation, dehydrogenation, aromatization, cych'zstion, thermal cracking, hydrodesulphurization, hydrodenitrogenation, peroxidation, deashing and haloge;:iation. The properties that are controlled could include Mooney viscosity, polymer unsaturation, comonomer incorporation, h;uogen content, polymer concentration, monomer concentration, molecular weight, melt index stream component composition, moisture in the product, and molecular weight distribution. Depending upon the process, the it. situ measurement made in addition to the spectr?! measurements could include, among others, temperature, viscosity, pressure, density, refractive index, pH value, conductance and dielectric constant. These and other similar variations in the process, property being controlled and measurements made for predictkm of the property being controlled are intended to be within the scope of the invention We claim: 1. A method for online control of a process to produce a product with a property P having a desired value D comprising: obtaining a set of measured spectra having measurement errors for a set of calibration samples at least one intermediate step in said process; correcting said measured spectra for said measurement errors to produce a set of corrected spectra for said set of calibration samples; determining a set of weights relating said corrected spectrum of each of said calibration samples to a set of orthonormal basis functions; obtaining a value of said property P for each of said calibration samples; determining a predictive model relating said value for said property P to said set of weights; measuring a spectrum for a test sample at said at least one intermediate step in said process; obtaining a corrected spectrum for said test sample at said at least one intermediate step; determining an estimated value E for said property P for said test sample from said predictive model and said corrected spectrum of said test sample and controlling said process using a calculated difference between said estimated value E and said value D. 2. The method as claimed in claim 1 wherein said product comprises a polymer. 3. The method as claimed in claim 1 wherein said measured spectra are selected from the group consisting of Raman spectra, NMR spectra, and infrared spectra and absorbance spectra in the near infrared region. 4. The method as claimed in claim 3 wherein said predictive model is determined by a linear least squares regression. 5. The method as claimed in claim 3 wherein said process comprises a procedure chosen from the group consisting of polymerization, steam cracking, olefin purification, aromatic purification, isomerization, catalytic cracking, catalytic reforming, hydrogenation, oxidation, partial oxidation, dehydration, hydration, nitration, epoxidation, distillation, combustion, alkylation, neutralization, ammination, esterfication, dimerization, membrane separation, carbonylation, ketonization, hydroformulation, oligomerization, pyrolysis, solfonation, crystallization, adsorption, extractive distillation, hydrodealkylation, dehydrogenation, aromatization, cyclization, thermal cracking, hyclrodesulphurization, hydrodenitrogenation, peroxidation, deashing and halogenation. h. The method as claimed in claim 5 wherein said set of orthonormal basis functions characterizing said corrected absorbance spectra for said calibration samples comprise eigenspectra determined by a Singular Value Decomposition. 7. The method as claimed in claim 5 wherein said property P is selected from the group consisting of Mooney viscosity, polymer unsaturation, comonomer incorporation, halogen content, polymer concentration, monomer concentration, molecular weight, melt index, polymer density, stream component composition, moisture in the product and molecular weight distribution. 8. The method as claimed in claim 7 wherein said measurement of near infrared spectra are made by a Fourier Transform Near Infrared (FTNIR) spectrometer. 9. The method as claimed in claim 7 wherein said measurement of said spectrum for said test sample is performed at least once every two minutes. 10. A method for online control of a process substantially as herein described with reference to the accompanying drawings.

Documents:

3830-del-1997-abstract.pdf

3830-del-1997-claims.pdf

3830-del-1997-correspondence-others.pdf

3830-del-1997-correspondence-po.pdf

3830-del-1997-description (complete).pdf

3830-del-1997-drawings.pdf

3830-del-1997-form-1.pdf

3830-del-1997-form-13.pdf

3830-del-1997-form-19.pdf

3830-del-1997-form-2.pdf

3830-del-1997-form-3.pdf

3830-del-1997-form-4.pdf

3830-del-1997-form-6.pdf

3830-del-1997-gpa.pdf

3830-del-1997-petition-124.pdf

3830-del-1997-petition-137.pdf

3830-del-1997-petition-138.pdf