Chemical modification of starch can be used to produce environmentally degradable

flocculants. This is carried out at elevated temperature, which in turn causes thermal

degradation, influencing the quality of the product. Starting from experimental results

and the probabilistic nature of the process stochastic model was established to identify

the rate and the mechanism of degradation under various conditions. It was concluded

that applying moderate temperature around 143 Celsius ensures sufficient production rate

without excessive thermal degradation

Dencs, J.

Gyenis, János

Márton, G.

Nős, G.

English

Repository of the Academy's Library

ABSTRACTChemical modification of starch can be used to produce environmentally degradableflocculants. This is carried out at elevated temperature, which in turn causes thermaldegradation, influencing the quality of the product. Starting from experimental resultsand the probabilistic nature of the process stochastic model was established to identifythe rate and the mechanism of degradation under various conditions. It was concludedthat applying moderate temperature around 143 oC ensures sufficient production ratewithout excessive thermal degradation.INTRODUCTIONDuring chemical modification of starch to produce environmentally friendly flocculants,elevated temperature together with chemical agents are applied to achieve appropriateproduct. Heating generally causes thermal degradation, which is necessary to make theproduct soluble in water. However, beyond a certain extent of degradation, this maydisadvantageously influence the quality of the product. In order to prepare high qualityflocculants, we have to keep the highest possible molecular weight which is still soluble.To achieve this requirement, the conditions of treatment, namely temperature andprocessing time should be optimized. Besides experimental investigation, theoreticalstudy by modeling the process helps to achieve this objective.EXPERIMENTALSIn order to produce environmentally friendly anionic flocculants, degradable in thenature, corn waxy starch (Meritena 300, Hungrana, Hungary) was modified by partialsubstitution of the OH groups in the anhydroglucose monomer units with phosphategroups by laboratory experiments (Dencs et al. 2003). The chemical reaction wasperformed in the solid phase at elevated temperatures, where the phosphorylation andheat degradation of the starch took place simultaneously. Due to this process, themolecular weight distribution has decreased from the original 50-100,000 MDa to a rangebetween 1-20 MDa.At first, the native starch samples were impregnated with the aqueous solution of sodiumdiammonium phosphate and with N-containing catalysts in mortar. Then the samplesSession: Modeling and SimulationProcessesIdentification of Thermal Degradation Process of Starch inProduction of Environmentally Friendly FlocculantsJ. Gyenisa*, J. Dencsb, G. Nősc, G. Martonda Research Institute of Chemical and Process Engineering, University of Veszprém,8200 Veszprém, Hungary, Email: gyenis@omega.richem.hub Department of Chemical Engineering, University of Veszprém, 8200 Veszprém,Hungary, Email: dencsj@almos.vein.huc Department of Chemical Engineering, University of Veszprém, 8200 Veszprém,Hungary, Email: gnosi@almos.vein.hud Department of Chemical Engineering, University of Veszprém, 8200 Veszprém,Hungary, Email: martongy@almos.vein.huKeywords: Starch, thermal degradation, stochastic model, flocculants.CHEMCON–05,NewDelhi2were dried and put into a block thermostat at various temperatures for different periods.The applied temperatures and processing times were changed between 135 and 150 oC,and from 5 to 270 minutes, respectively, as shown in Table 1.Table 1: Experimental conditionsDuration of the heat treatment, minutes5 10 15 20 25 30 40 50 60 75 90 105 120 150 180 210 240 270135 + + + + + + + + + + + + + + + +138 + + + + + + + + + + + + + + + +140 + + + + + + + + + + + + + + +143 + + + + + + + + + + + + + + + +145 + + + + + + + + + + + + + + + +148 + + + + + + + + + + + + + + + +Temperature,oC150 + + + + + + + + + + + + +The progress of the chemical reaction was followed by determining the chemically boundphosphorous content in the starch phosphate samples. After extracting the free phosphate,the analysis was carried out from the ash by Biochrom 4060 spectrophotometer. Theelectric charge density of the obtained starch phosphates, as well as their flocculationcharacteristics were studied in separate study, discussed by Dencs et al., (2003).The degradation was examined by measuring the molecular weight distributions in theresulted products. For this, starch phosphate samples of identical weights were dissolvedin distilled water at 95 oC for 24 h (0.05 g starch in 5 mL water) and were analyzed byHPSEC/MALLS/RI. The relative quantities of the soluble part of the samples, i.e. theoutput of phosphorylation and degradation process were determined from the area belowthe RI chromatograms. The molecular weight distributions were obtained from the shapesof the chromatograms by Discovery 32 software. The mean molecular weights of theproducts were calculated both on weight and number averaging basis.From the results of the laboratory experiments it was concluded that, apart from a fewcontroversial data in the initial period, the mean molecular weight was decreasing withincreasing processing time and temperature as seen in Fig. 1. The output of the solublecompounds in the treated samples relative to the maximal achievable product quantitywas increasing with rising temperature and by elongating the treatment. Fig. 2 shows theFig.1: Change of the mean molecular weight during the treatments0246810121416180 50 100 150 200 250 300time, minmeanmolecularweight,MDa 135 oC 138 oC140 oC 143 oC145 oC 148 oC150 oC3t = 0 min0.00.20.40.60.81.00 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyt = 150 min0.00.10.20.30.40.50.60 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyt = 240 min0.00.10.20.30.40.50.60 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyt = 90 min0.00.10.20.30.40.50.60 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyt = 10 min0.00.10.20.30.40.50.60 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyt = 50 min0.00.10.20.30.40.50.60 2 4 6 8 10 12 14 16 18 20<molecular weight, MDaFrequencyrelative areas below the RI chromatograms relative to the maximal achievable productquantity, in function of time and temperature.Depending on the conditions, the molecular weight distributions of the products were alsochanging. Fig. 3 shows some typical examples obtained at 143 oC after different treatingtimes. To explain these variations, creation of an adequate mathematical model wasinevitable. The basic principles for modeling were deduced from the results of theexperimental studies.MODELING OF THE DEGRADATION PROCESSFrom the results of the experiments it was recognized that, depending on the temperatureand duration of the treatment, the rate of degradation and the shapes of the resultantmolecular weight distributions were quite different. From respect of product quality notonly the mean molecular weight, but its standard deviation are important features: thenarrower is the distribution and the higher is its mean value, the better is the applicabilityfor flocculation processes.Fig.2: Dependence of the amount of soluble product on the process parametersFig.3: Examples for the changes of molecular weight distribution during the treatment0.00.20.40.60.81.00 50 100 150 200 250 300time, minnormalizedRIareas,-135 oC138 oC140 oC143 oC145 oC148 oC150 oC4In principle, considering various degradation mechanisms, different distributions couldevolve. From this respect, the location of splitting in the starch molecules has of primaryimportance. Starting from a narrow initial size distribution e.g., splitting at the middle ofthe molecules will lead to new size distribution, which will be also narrow. If splitting cantake place at any part of the molecule with uniform probability, the resultant newdistribution will be much broader. Deliberation of small groups from the molecules e.g.splitting at the ends of the chain will result in even broader and bimodal distribution.Therefore, supposing a given mechanism, the resultant molecular weight distributions canbe predicted by an adequate mathematical model. Or inversely, if the distributions areknown from experiments, the ruling degradation mechanism can be determined by themodel.Preliminary Considerations and NeglectionsIn creating the mathematical model the following suppositions were made: The splitting of a given molecule among the multitude of other species is a stochasticevent, i.e. it may happen accidentally. During a time interval, splitting has a givenprobability, depending on the chemical structure of the molecule, also influenced bythe conditions in the reaction mixture. The location of splitting within the molecule may also be accidental, meaning thatsplitting off different parts from the carbohydrate chain can happen with certainprobabilities, showing a probability distribution along the structure or length of themolecules. Due to the huge number of molecules present in the raw material or in the products,the molecular weight distribution can be considered quasi continuous one. However,to avoid the excessive complexity of the model, this can be simplified to a histogramby dividing the whole molecule weight range into discrete intervals. It was supposed that the transformation probabilities of a molecule belonging to agiven molecular weight interval to smaller ones depend only on its actual state and donot change with time. In other words, the degradation process can be considered ashomogeneous Markov chain. The variation of molecular weight or chemical structure within a discrete interval wasneglected. It was also supposed that the transformation of the molecules goes on by splittingonly. If, however, polymerization also happens, this can be considered by loweringthe net degradation probability values, essentially not affecting the use of the model.The Applied ModelBased on the considerations above, a new theoretical model was constructed for thestudied degradation process, based on the stochastic approach suggested by Inoue andYamaguchi (1970) and by Gyenis and Kátai (1990) for solids mixing problems.The principle of the model is shown in Fig. 4, considering four discrete molecular weightintervals (M1-M4) for the products and one for the raw material (M0). The boxes in theFigure correspond to the mass fractions of material belonging to given intervals at a givenmoment and the arrows between them represent the transition of certain material fractionsbetween the intervals. At the start, all the material to be treated (but not necessarily all)can be in interval M0. Within an arbitrary time step during the degradation processvarious fractions of this material can be transferred to other intervals of lower molecularweights (M1, M2, M3, M4), according to their transition probabilities.5Since the process is considered as stochastic one, the temporary distribution of thematerial among the different molecular weight intervals after the nth time step (in timeinterval tn) is also probabilistic, thus represented by a state probability vector p(tn), wherepi(tn) is the probability that unit quantity of material belongs to the ith molecular weightinterval at that time. )()()()()()( 43210 nnnnnn tptptptptptp  (1)This state probability vector corresponds to the expectable weight distribution of thematerial among the different intervals at a given time.The chance that unit quantity of material belonging to the ith molecular weight intervalwill be transformed to the jth interval during a time step t is denoted by transitionprobability Pi,j. Diagonal elements Pi,i denote the probability of remaining in the ithinterval. All these probabilities are summarized in a transition probability matrix P.4,44,33,24,14,03,33,23,13,02,22,12,01,11,00,00000000000PPPPPPPPPPPPPPPP (2)The expected weight distribution of the material or the state probability after the nth timestep can be calculated by Eqn. (3), accordingly to other stochastic models used e.g. byInoue and Yamaguchi (1970):nn Ptptp  )()( 0 (3)where )( 0tp is the initial state probability, or initial molecular weight distribution.Fig.4: Transitions between the different molecular weight intervalsM1M3M2M4M0F1,F1,F1, F2,F2,F3,F0,F0, F0,F0,6The expected value of the transformed mass fraction Fij from the ith to the jth interval inthe nth time interval is:jiniij PtpF ,)(  (4)Decreasing the time step t infinitesimally, the calculation can be carried out by solvinga differential equation system where one of the composing equations referring to thechange of state probability pi(t) is:  ijjjiiijjjiji PppPdtdp,,,, (5)RESULTS AND DISCUSSIONTo define the molecular weight intervals by dividing the whole size range into severalones, any kind of division e.g. linear, exponential, or even not regular scale can be used.Considering the measured distribution data where very small molecule fragments weregenerally absent for a considerable long period after starting the process, it was supposedthat fragmentation takes place by splitting the molecules around their middle region.Therefore, in the study reported here exponential scale was used by halving the intervallimits repeatedly. Four intervals were defined for the degradation products, rangingbetween 16-32 (M1), 8-16 (M2), 4-8 (M3), and 2-4 (M4) million Dalton (MDa),respectively. Fragments below 2 MDa were measured in a few cases only, in negligiblequantities. Therefore, no interval below 2 MDa was considered in the model. Measureddata between 32-16 MDa had some uncertainty, because of the limited water solubility ofthe material above 20 MDa during sample preparation for the analysis.Since the original starch was insoluble in water, the same difficulty arose regarding itsanalysis by the applied HPSEC/MALLS/RI method without chemical modification. Thelack of knowledge on its exact molecular weight distribution constrained us to considerthe first M0 compartment as a black box with 32 MDa lower and indefinite upper limits.To normalize the distribution data in the M0-M4 intervals, the maximal achievableamount of the water soluble product was used as normalizing factor, keeping the materialbalance.In order to investigate the degradation process, computer simulations were carried out byModelMaker software (Cherwell Scientific) using the model described above. The valuesof the transition and state probabilities were found out by fitting the experimental andsimulated data using trial-and-error method. As a result, quite good agreement wasachieved between the measured and simulated data (0.05–0.07 w/w or 5.2–7.5 per centrelative standard deviation) for the studied conditions. Fig. 5 shows a typical example forthe variation of the measured and simulated weight fractions in function of time forvarious molecular size intervals, obtained for heat treatment at 145 oC.Table 2 shows the transition probabilities obtained for the best fits of the experimentaland simulated data. The transition probabilities shown in Fig. 4, but not listed in Table 2proved to be zero. From the values shown in Table 2 the degradation probabilities Pi ofthe material in any ith molecular weight interval were determined from the Pi,j values byEqn. 6.7jjii PP , (6)0.00.20.40.60.81.00 50 100 150 200 250time, minweigthfracion,w/w>32 MDa measured >32 MDa calculated16-32 MDa measured 16-32 MDa calculated8-16 MDa measured 8-16 MDa calculated4-8 MDa measured 4-8 MDa calculated2-4 MDa measured 2-4 MDa calculatedTable 2: Transition probabilities between different molecular weight intervalsTemperature of the heat treatment135 oC 138 oC 140 oC 143 oC 145 oC 148 oC 150 oCP0,1 0.0020 0.0050 0.0010 0.0090 0.0020 0.0080 0.0010P0,2 0.0100 0.0100 0.0150 0.0120 0.0150 0.0130 0.0150P0,3 0.0000 0.0000 0.0010 0.0000 0.0025 0.0020 0.0150P1,2 0.0003 0.0003 0.0013 0.0013 0.0100 0.0050 0.0100P1,3 0.0030 0.0070 0.0130 0.0130 0.0250 0.0120 0.0250P2,3 0.0060 0.0035 0.0075 0.0035 0.0075 0.0060 0.0060P2,4 0.0000 0.0002 0.0012 0.0015 0.0012 0.0005 0.0005Transitionprobabilities,w/w/minP3,4 0.0015 0.0020 0.0032 0.0045 0.0060 0.0065 0.0120Degradation probabilities calculated by Eqn. 6 showed significant rise with increasingtemperature, and were decreasing with diminishing molecular weights as shown in Fig. 6.Fig.5: Measured and simulated data for heat treatment at 145 oC0.000.010.020.030.040.05130 135 140 145 150 155temperature, oCdegradationprobability,min-1M0 M1M2 M30.000.010.020.030.040.05M0 M1 M2 M3 M4molecular weight fraction, MDadegradationprobability,min-1 135 oC 138 oC140 oC 143 oC145 oC 148 oC150 oCFig.6: Degradation probabilities depending on the temperature and molecular weighta b8Considering the transformation probabilities from given molecular weight intervals toother intervals, the ruling mechanism of the process can also be identified. Moleculeswith lower sizes (intervals M2 and M3) split most probably into two pieces, around theirmiddle, because the daughter molecules will mainly be transferred into the next intervals(M3 and M4), respectively. However, from the intervals of higher molecular weights (M0and M1) the most probable transformation takes place towards the second next intervals(to M2 and M3, respectively), suggesting that these molecules break most likely into 3 ormore pieces, thus not always at their middle region. From the intervals of high molecularweight direct formation of small fragments has no probability at all.CONCLUSIONSFrom the results of simulation it was concluded that the degradation probabilities in thestudied 135-150 oC temperature range were increasing with increasing temperature andwere reduced significantly with decreasing molecular weight.From the probabilities of transitions from given molecular weight intervals to others,conclusions were drawn to the probable mechanism of the degradation.Studying the molecular weight distribution of the products obtained at differenttemperatures and treating times, it was concluded that moderate heat treatment around143 oC results in sufficiently high production of water soluble starch derivate (about 80% of the maximal achievable yield) within reasonable time (75 min). The productobtained here had 12 MDa mean molecular weight and relatively narrow size distributionbetween 7 and 20 MDa (with STD 4.3), suitable for environmentally benign flocculants.NOMENCLATUREMi ith molecular weight intervalFij weight fraction transformed from interval i to interval j in unit time, w/w/minp(tn) state probability vector defined by Eqn.1.pi(tn) probability that unit quantity of material belongs to the ith interval, dimensionlessPij transition probability from an ith to a jth interval during at time step, w/w/minP transition probability matrix defined by Eqn.2.tn time after the nth time step, minΔt time step, minREFERENCESDencs, J., Dencs, B., Marton, G., and Nős, G., 2003, Investigation of Solid Phase StarchModification Reaction, Chem. Eng. Res. Dev., 82(A2) 215-219.Gyenis, J., and Kátai, F., 1990, Determination and Randomness in Mixing of ParticulateSolids, Chem. Eng. Sci., 45(9), 2843-2855.Inoue I., and Yamaguchi, K., 1970, Particle Motion in a Mixer. Mixing in a TwoDimensional V-Type Mixer, Int. Chem. Engng., 10(3), 490-497.

Identification of Thermal Degradation Process of Starch in Production of Environmentally Friendly Flocculants

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