ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Wed, 16 Oct 2019 10:17:30 GMT2019-10-16T10:17:30Z50191- Modeling of an industrial wiped film poly(ethylene terephthalate) reactorhttps://scholarbank.nus.edu.sg/handle/10635/92133Title: Modeling of an industrial wiped film poly(ethylene terephthalate) reactor
Authors: Bhaskar, V.; Gupta, S.K.; Ray, A.K.
Abstract: An improved two-phase model has been developed for a wiped film (third stage) polyester reactor. The model accounts for all the important main and side reactions and incorporates the effect of vaporization of four low molecular weight volatile species. Industrial data under three different operating conditions are used to obtain best-fit (tuned) values of the model parameters. When these values are used unchanged in the model, the latter predicts industrial operation under a fourth set of operating condition. This indicates that the 'tuned' model accounts for all the physico-chemical phenomena present in the reactor. A sensitivity study reveals the importance of some parameters and suggests that these should be determined experimentally using more basic experimental studies rather than by tuning industrial data wherein several additional physical phenomena are present.
Tue, 01 May 2001 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921332001-05-01T00:00:00Z
- Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptationshttps://scholarbank.nus.edu.sg/handle/10635/89534Title: Multi-objective optimization of the operation of an industrial low-density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations
Authors: Agrawal, N.; Rangaiah, G.P.; Ray, A.K.; Gupta, S.K.
Abstract: In this study, a comprehensive model for an industrial low-density polyethylene (LDPE) tubular reactor is presented. The model parameters are tuned using industrial data on the temperature profile, the monomer conversion and the number-average molecular weight at the end of the reactor, and estimates of the several side products from the reactor. Complete details of the model are provided. Thereafter, a two-objective optimization of this LDPE reactor is performed; the monomer conversion is maximized while the sum of the normalized concentrations of the three important side products (methyl, vinyl, and vinylidene groups) is minimized. Three variants of the binary-coded non-dominated sorting genetic algorithm - namely, NSGA-II, NSGA-II-JG, and NSGA-II-aJG - are used to solve the optimization problem. The decision variables used for optimization include the following: the feed flow rates of the three initiators and of the transfer agent, the inlet temperature, the inlet pressure, and the average temperatures of the fluids in the five jackets. Also, the temperature of the reaction mass is constrained to lie below a safe value. An equality constraint is used for the number-average molecular weight (M n,f) of the product, to ensure product quality. Pareto-optimal solutions are obtained. It is observed that the algorithms converge to erroneous local optimal solutions when hard equality constraints such as Mn,f = desired number-average molecular weight (Mn,d) are used. Correct global optimal Pareto sets are obtained by assembling appropriate solutions from several problems involving softer constraints of the type Mn,f = Mn,d ± an arbitrary number. Furthermore, the binary-coded NSGA-II-aJG and NSGA-II-JG perform better than NSGA-II near the hard end-point constraints. The solution of a four-objective problem (with each of the three normalized side product concentrations taken individually as objective functions) is comparable to that of the two-objective problem, and the former (more) computationally intensive problem does not need to be solved. © 2006 American Chemical Society.
Wed, 26 Apr 2006 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/895342006-04-26T00:00:00Z
- Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiencyhttps://scholarbank.nus.edu.sg/handle/10635/92481Title: Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiency
Authors: Ravi, G.; Viswanathan, S.; Gupta, S.K.; Ray, M.B.
Abstract: Multi-objective optimization of a venturi scrubber was carried out using a three-dimensional model for collection efficiency and non-dominated sorting genetic algorithm (NSGA). Two objective functions, namely (a) maximization of the overall collection efficiency, and (b) minimization of the pressure drop were used in this study. Three decision variables including two operating parameters, viz liquid-gas ratio and gas velocity in the throat, and the nozzle configuration, which takes into account the three-dimensional nature of the problem, were used in the optimization. Optimal design curves (non-dominated Pareto sets) and the values of the decision variables corresponding to optimum conditions on the Pareto set for a pilot-scale scrubber were obtained. The liquid to gas (L/G) ratio, which is a key decision variable that determines the uniformity of liquid distribution, and staggered nozzle configuration can produce uniform liquid distribution in the scrubber. Multiple penetration using nozzles of two different sizes in a triangular staggered arrangement can reduce liquid loading by as much as 50%, consequently reducing the pressure drop in the scrubber. © 2003 Society of Chemical Industry.
Sat, 01 Feb 2003 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/924812003-02-01T00:00:00Z
- Multi-objective optimization of membrane separation modules using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92162Title: Multi-objective optimization of membrane separation modules using genetic algorithm
Authors: Yuen, C.C.; Aatmeeyata; Gupta, S.K.; Ray, A.K.
Abstract: Hollow fiber membrane separation modules are used extensively in industry for a variety of separation processes. In most cases, conflicting requirements and constraints govern the optimal choice of decision (or design) variables. In fact, these optimization problems may involve several objectives, some of which must be maximized, while the others minimized simultaneously. Often, a set of equally good (non-dominated or Pareto optimal) solutions exist. In this study, a membrane separation module for the dialysis of beer has been taken as an example system to illustrate the multi-objective optimization of any membrane module. A mathematical model is first developed and 'tuned' using some experimental results available in the literature. The model is then used to study a few simple multi-objective optimization problems using the non-dominated sorting genetic algorithm (NSGA). Two objective functions are used: the alcohol removal (%) from the beer is maximized, while simultaneously minimizing the removal of the 'extract' (taste chemicals). Pareto optimal solutions are obtained for this module. It was found that the inner radius of the hollow fiber is the most important decision variable for most cases. Another optimization problem using the cost as the third objective function is also solved, using a combination of the ε-constraint method and NSGA. It is found that the Pareto solutions lie on a curve in the three-dimensional objective function space, and do not form a surface. Copyright (C) 2000 Elsevier Science B.V.
Sun, 20 Aug 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921622000-08-20T00:00:00Z
- Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiencyhttps://scholarbank.nus.edu.sg/handle/10635/75202Title: Multi-objective optimization of venturi scrubbers using a three-dimensional model for collection efficiency
Authors: Ravi, G.; Viswanathan, S.; Gupta, S.K.; Ray, M.B.
Abstract: Multi-objective optimization of a venturi scrubber was carried out using a three-dimensional model for collection efficiency and non-dominated sorting genetic algorithm (NSGA). Two objective functions, namely (a) maximization of the overall collection efficiency, and (b) minimization of the pressure drop were used in this study. Three decision variables including two operating parameters, viz liquid-gas ratio and gas velocity in the throat, and the nozzle configuration, which takes into account the three-dimensional nature of the problem, were used in the optimization. Optimal design curves (non-dominated Pareto sets) and the values of the decision variables corresponding to optimum conditions on the Pareto set for a pilot-scale scrubber were obtained. The liquid to gas (L/G) ratio, which is a key decision variable that determines the uniformity of liquid distribution, and staggered nozzle configuration can produce uniform liquid distribution in the scrubber. Multiple penetration using nozzles of two different sizes in a triangular staggered arrangement can reduce liquid loading by as much as 50%, consequently reducing the pressure drop in the scrubber. © 2003 Society of Chemical Industry.
Sat, 01 Feb 2003 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/752022003-02-01T00:00:00Z
- Determining the flow characteristics of a power law liquidhttps://scholarbank.nus.edu.sg/handle/10635/66524Title: Determining the flow characteristics of a power law liquid
Authors: Hillier, J.R.; Ting, D.; Kopplin, L.L.; Koch, M.; Gupta, S.K.
Abstract: An experiment was developed which used the easily understood macroscopic energy balance to obtain the experimental results. This experimental set-up was developed to study the decrease of the apparent viscosity of a 0.07% aqueous solution of a sodium salt of carboxymethyl cellulose (Na-CMC) with increasing shear rates. The mass-average velocity of the solution inside the capillary was obtained using the continuity equation. The additional experimental data was obtained easily after the addition of sodium chloride to the CMC solution, to study the effect of molecular concentration of the polyelectrolyte.
Sun, 01 Sep 2002 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/665242002-09-01T00:00:00Z
- On-Line Optimization of Free Radical Bulk Polymerization Reactors in the Presence of Equipment Failurehttps://scholarbank.nus.edu.sg/handle/10635/66723Title: On-Line Optimization of Free Radical Bulk Polymerization Reactors in the Presence of Equipment Failure
Authors: Garg, S.; Gupta, S.K.; Saraf, D.N.
Abstract: An on-line optimizing control scheme has been developed for bulk polymerization of free radical systems. The effects of random errors, as well as one kind of a major disturbance (heating system failure), have been studied. A model-based, inferential state estimation scheme was incorporated to estimate, on-line, the parameters of the model (and thereby, the monomer conversion and molecular weight of the polymer) using experimental data on temperature and viscosity. A sequential quadratic programming technique was used for this purpose. A major disturbance, such as heating system failure, leads to a deteriorated final product unless an on-line optimal temperature trajectory (history) is recomputed and implemented on the reactor. Genetic algorithm was used for this purpose. It has been found that, if the "sensing" of the major temperature deviation from the optimal value and rectification of the heating system is achieved well in advance of the onset of the Trommsdroff effect, use of a reoptimized temperature history is sufficient to produce the desired product without significantly altering reaction time. However, if such a disturbance occurs late, a single-shot intermediate addition of an optimal amount of initiator needs to be used in addition to changing the temperature history to produce polymers having the desired properties in the minimum reaction time. Other types of failures can similarly be handled using the methodology developed. © 1999 John Wiley & Sons, Inc.
Sun, 21 Mar 1999 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/667231999-03-21T00:00:00Z
- Modeling of an industrial wiped film poly(ethylene terephthalate) reactorhttps://scholarbank.nus.edu.sg/handle/10635/66676Title: Modeling of an industrial wiped film poly(ethylene terephthalate) reactor
Authors: Bhaskar, V.; Gupta, S.K.; Ray, A.K.
Abstract: An improved two-phase model has been developed for a wiped film (third stage) polyester reactor. The model accounts for all the important main and side reactions and incorporates the effect of vaporization of four low molecular weight volatile species. Industrial data under three different operating conditions are used to obtain best-fit (tuned) values of the model parameters. When these values are used unchanged in the model, the latter predicts industrial operation under a fourth set of operating condition. This indicates that the 'tuned' model accounts for all the physico-chemical phenomena present in the reactor. A sensitivity study reveals the importance of some parameters and suggests that these should be determined experimentally using more basic experimental studies rather than by tuning industrial data wherein several additional physical phenomena are present.
Tue, 01 May 2001 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/666762001-05-01T00:00:00Z
- Multiobjective optimization of steam reformer performance using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/66700Title: Multiobjective optimization of steam reformer performance using genetic algorithm
Authors: Rajesh, J.K.; Gupta, S.K.; Rangaiah, G.P.; Ray, A.K.
Abstract: An existing side-fired steam reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, 'optimal' conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.
Sat, 01 Jan 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/667002000-01-01T00:00:00Z
- Multi-objective optimization of membrane separation modules using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/66697Title: Multi-objective optimization of membrane separation modules using genetic algorithm
Authors: Yuen, C.C.; Aatmeeyata; Gupta, S.K.; Ray, A.K.
Abstract: Hollow fiber membrane separation modules are used extensively in industry for a variety of separation processes. In most cases, conflicting requirements and constraints govern the optimal choice of decision (or design) variables. In fact, these optimization problems may involve several objectives, some of which must be maximized, while the others minimized simultaneously. Often, a set of equally good (non-dominated or Pareto optimal) solutions exist. In this study, a membrane separation module for the dialysis of beer has been taken as an example system to illustrate the multi-objective optimization of any membrane module. A mathematical model is first developed and 'tuned' using some experimental results available in the literature. The model is then used to study a few simple multi-objective optimization problems using the non-dominated sorting genetic algorithm (NSGA). Two objective functions are used: the alcohol removal (%) from the beer is maximized, while simultaneously minimizing the removal of the 'extract' (taste chemicals). Pareto optimal solutions are obtained for this module. It was found that the inner radius of the hollow fiber is the most important decision variable for most cases. Another optimization problem using the cost as the third objective function is also solved, using a combination of the ε-constraint method and NSGA. It is found that the Pareto solutions lie on a curve in the three-dimensional objective function space, and do not form a surface. Copyright (C) 2000 Elsevier Science B.V.
Sun, 20 Aug 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/666972000-08-20T00:00:00Z
- Multiobjective optimization of an industrial wiped-film pet reactorhttps://scholarbank.nus.edu.sg/handle/10635/66695Title: Multiobjective optimization of an industrial wiped-film pet reactor
Authors: Bhaskar, V.; Gupta, S.K.; Ray, A.K.
Abstract: Multiobjective optimization of a third-stage, wiped-film polyester reactor was carried out using a model that describes an industrial poly(ethylene terephthalate) reactor quite well. The two objective functions minimized are the the acid and vinyl end group concentrations in the product. These are two of the undesirable side products produced in the reactor. The optimization problem incorporates an endpoint constraint to produce a polymer with a desired value of the degree of polymerization. In addition, the concentration of the di-ethylene glycol end group in the product is constrained to lie within a certain range of values. Adaptations of the nondominated sorting genetic algorithm have been developed to obtain optimal values of the five decision variables: reactor pressure, temperature, catalyst concentration, residence time of the polymer inside the reactor, and the speed of the agitator. The optimal solution was a unique point (no Pareto set obtained). Problems of multiplicity and premature convergence were encountered. A 'smoothening' procedure is suggested to generate near-optimal operating conditions. The optimal solution corresponds simultaneously to minimum values of the residence time of the polymeric reaction mass in the reactor.
Mon, 01 May 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/666952000-05-01T00:00:00Z
- Multiobjective optimization of cyclone separators using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92160Title: Multiobjective optimization of cyclone separators using genetic algorithm
Authors: Ravi, G.; Gupta, S.K.; Ray, M.B.
Abstract: Multiobjective optimization of a set of N identical reverse-flow cyclone separators in parallel was carried out by using the nondominated sorting genetic algorithm (NSGA). Two objective functions were used: the maximization of the overall collection efficiency and the minimization of the pressure drop. Nondominated Pareto optimal solutions were obtained for an industrial problem in which 165 m3/s of air was treated. In addition, optimal values of several decision variables, such as the number of cyclones and eight geometrical parameters of the cyclone, are obtained. The study shows that the diameters of the cyclone body and the vortex finder, and the number of cyclones used in parallel, are the important decision variables influencing the optimal solutions. This study illustrates the applicability of NSGA in solving multiobjective optimization problems involving gas-solid separations.
Wed, 01 Nov 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921602000-11-01T00:00:00Z
- Multiobjective optimization of steam reformer performance using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92165Title: Multiobjective optimization of steam reformer performance using genetic algorithm
Authors: Rajesh, J.K.; Gupta, S.K.; Rangaiah, G.P.; Ray, A.K.
Abstract: An existing side-fired steam reformer is simulated using a rigorous model with proven reaction kinetics, incorporating aspects of heat transfer in the furnace and diffusion in the catalyst pellet. Thereafter, 'optimal' conditions, which could lead to an improvement in its performance, are obtained. An adaptation of the nondominated sorting genetic algorithm is employed to perform a multiobjective optimization. For a fixed production rate of hydrogen from the unit, the simultaneous minimization of the methane feed rate and the maximization of the flow rate of carbon monoxide in the syngas are chosen as the two objective functions, keeping in mind the processing requirements, heat integration, and economics. For the design configuration considered in this study, sets of Pareto-optimal operating conditions are obtained. The results are expected to enable the engineer to gain useful insights into the process and guide him/her in operating the reformer to minimize processing costs and to maximize profits.
Sat, 01 Jan 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921652000-01-01T00:00:00Z
- Multiobjective optimization of the continuous casting process for poly (methyl methacrylate) using adapted genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92166Title: Multiobjective optimization of the continuous casting process for poly (methyl methacrylate) using adapted genetic algorithm
Authors: Zhou, F.; Gupta, S.K.; Ray, A.K.
Abstract: The nondominated sorting genetic algorithm (NSGA) has been used to optimize the operation of the continuous casting of a film of poly (methyl methacrylate). This process involves two reactors, namely, an isothermal plug flow tubular reactor (PFTR) followed by a nonisothermal film reactor. Two objective functions have been used in this study: the cross-section average value of the monomer conversion, x̄mf, of the product is maximized, and the length, zf, of the film reactor is minimized. Simultaneously, the cross-section average value of the number-average molecular weight of the product is forced to have a certain prescribed (desired) value. It is also ensured that the temperature at any location in the film being produced lies below a certain value, to avoid degradation reactions. Seven decision variables are used in this study: the temperature of the isothermal PFTR, the flow rate of the initiator in the feed to the PFTR (for a specified feed flow rate of the monomer), the film thickness, the monomer conversion at the output of the PFTR, and three coefficients describing the wall temperature to be used in the film reactor. Sets of nondominating (equally good) optimal solutions (Pareto sets) have been obtained due to the conflicting requirements for the several conditions studied. It is interesting to observe that under optimal conditions, the exothermicity of the reactions drives them to completion near the center of the film, while heat conduction and higher wall temperature help to achieve this in the outer regions.
Wed, 01 Nov 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921662000-11-01T00:00:00Z
- On-Line Optimization of Free Radical Bulk Polymerization Reactors in the Presence of Equipment Failurehttps://scholarbank.nus.edu.sg/handle/10635/92190Title: On-Line Optimization of Free Radical Bulk Polymerization Reactors in the Presence of Equipment Failure
Authors: Garg, S.; Gupta, S.K.; Saraf, D.N.
Abstract: An on-line optimizing control scheme has been developed for bulk polymerization of free radical systems. The effects of random errors, as well as one kind of a major disturbance (heating system failure), have been studied. A model-based, inferential state estimation scheme was incorporated to estimate, on-line, the parameters of the model (and thereby, the monomer conversion and molecular weight of the polymer) using experimental data on temperature and viscosity. A sequential quadratic programming technique was used for this purpose. A major disturbance, such as heating system failure, leads to a deteriorated final product unless an on-line optimal temperature trajectory (history) is recomputed and implemented on the reactor. Genetic algorithm was used for this purpose. It has been found that, if the "sensing" of the major temperature deviation from the optimal value and rectification of the heating system is achieved well in advance of the onset of the Trommsdroff effect, use of a reoptimized temperature history is sufficient to produce the desired product without significantly altering reaction time. However, if such a disturbance occurs late, a single-shot intermediate addition of an optimal amount of initiator needs to be used in addition to changing the temperature history to produce polymers having the desired properties in the minimum reaction time. Other types of failures can similarly be handled using the methodology developed. © 1999 John Wiley & Sons, Inc.
Sun, 21 Mar 1999 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921901999-03-21T00:00:00Z
- Multiobjective optimization of an industrial nylon-6 semibatch reactor system using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92156Title: Multiobjective optimization of an industrial nylon-6 semibatch reactor system using genetic algorithm
Authors: Gupta, R.R.; Gupta, S.K.
Abstract: Multiobjective Pareto optimal solutions for three different grades of nylon-6 produced in an industrial semibatch reactor are obtained by using the adapted Non-dominated Sorting Genetic Algorithm (adapted NSGA). The two objective functions minimized are the total reaction time and the concentration of undesirable cyclic dimer in the product, while simultaneously attaining desired values of the monomer conversion and the number average chain length. The control variables used are the fractional valve opening f(t) and the jacket fluid temperature TJ. The study shows a marked improvement over current industrial operation. It is found that the optimal values of the cyclic dimer concentration in the product are worse (higher) when the reactor-control valve system is studied than when the reactor is considered alone. This is because the control valve leads to additional constraints. The technique used is quite general and can be used to study other reactor systems as well.
Fri, 01 Jan 1999 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921561999-01-01T00:00:00Z
- Multiobjective optimization of a free radical bulk polymerization reactor using genetic algorithmhttps://scholarbank.nus.edu.sg/handle/10635/92155Title: Multiobjective optimization of a free radical bulk polymerization reactor using genetic algorithm
Authors: Garg, S.; Gupta, S.K.
Abstract: A multiobjective optimization technique has been developed for free radical bulk polymerization reactors using genetic algorithm. The polymerization of methyl methacrylate in a batch reactor has been studied as an example. The two objective functions which are minimized are the total reaction time and the polydispersity index of the polymer product. Simultaneously, end-point constraints are incorporated to attain desired values of the monomer conversion (xm) and the number average chain length (μn). A nondominated sorting genetic algorithm (NSGA) has been adapted to obtain the optimal control variable (temperature) history. It has been shown that the optimal solution converges to a unique point and no Pareto set is obtained. It has been observed that the optimal solution obtained using the NSGA for multiobjective function optimization compares very well with the solution obtained using the simple genetic algorithm (SGA) for a single objective function optimization problem, in which only the total reaction time is minimized and the two endpoint constraints on xm and μn are satisfied. © Wiley-VCH Verlag GmbH, 1999.
Fri, 01 Jan 1999 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921551999-01-01T00:00:00Z
- Multiobjective optimization of an industrial wiped-film pet reactorhttps://scholarbank.nus.edu.sg/handle/10635/92159Title: Multiobjective optimization of an industrial wiped-film pet reactor
Authors: Bhaskar, V.; Gupta, S.K.; Ray, A.K.
Abstract: Multiobjective optimization of a third-stage, wiped-film polyester reactor was carried out using a model that describes an industrial poly(ethylene terephthalate) reactor quite well. The two objective functions minimized are the the acid and vinyl end group concentrations in the product. These are two of the undesirable side products produced in the reactor. The optimization problem incorporates an endpoint constraint to produce a polymer with a desired value of the degree of polymerization. In addition, the concentration of the di-ethylene glycol end group in the product is constrained to lie within a certain range of values. Adaptations of the nondominated sorting genetic algorithm have been developed to obtain optimal values of the five decision variables: reactor pressure, temperature, catalyst concentration, residence time of the polymer inside the reactor, and the speed of the agitator. The optimal solution was a unique point (no Pareto set obtained). Problems of multiplicity and premature convergence were encountered. A 'smoothening' procedure is suggested to generate near-optimal operating conditions. The optimal solution corresponds simultaneously to minimum values of the residence time of the polymeric reaction mass in the reactor.
Mon, 01 May 2000 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/921592000-05-01T00:00:00Z
- Study of parametric sensitivity in an autothermal nylon 6 reactorhttps://scholarbank.nus.edu.sg/handle/10635/92354Title: Study of parametric sensitivity in an autothermal nylon 6 reactor
Authors: Majumdar, N.; Gupta, S.K.
Abstract: The parametric sensitivity of an industrial autothermal nylon 6 reactor was studied. The sensitivities of the temperature maxima with respect to various parameters of the model are computed numerically. The sensitivity peaks were found to occur (almost) at the same value of the input parameter, thus confirming the generalized nature of the thermal parametric sensitivity criterion. It is shown that this criterion can easily be used to find safer regions of operation of the reactor. The variation of the number-average chain length of the product, μnf, with the variation of input parameter, Wo, was also studied. A methodology was suggested to obtain the desired ranges of operation of the reactor which represent an optimal balance between the thermal sensitivity and the sensitivity of μnf.
Fri, 01 Jan 1999 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/923541999-01-01T00:00:00Z