Title of Invention  METHOD FOR OPTIMIZING PROCESS PRODUCTION PARAMETER 

Abstract  A method for optimizing chemical process parameters for producing at least two products from a production site to satisfy a given product demand for each of said products within a range of a temperature with which the rate of product production required to satisfy such demand is correlated to heat energy consumption and varied in accordance with a predetermined schedule of production to minimize the cost of heat energy consumed in a said process, with the cost of such heat energy dependent upon variable parameters comprising the steps of: formulating a process parameters for said production site which characterizes [he operating characteristics of the production site as a functional relationship between the rate of production of each of said products from said site, including batch wise in the production of such products, and the amount of energy consumed in the manufacture of each of said products. 
Full Text  FORM 2 THE PATENTS ACT, 1970 COMPLETE SPECIFICATION (SECTION 10) HEURISTIC METHOD FOR CHEMICALPROCESS OPTIMIZATION (i) AGRAWAL SANJEEV RADHAKRISHNA (ii) BAFNA SHEETAL KANTILAL (iii) CHANDRAHASAN PRASANTH (iv) SAXENA PRABAL KRISHNAKUMAR, ALL INDIAN NATIONALS, C/O. AGRAWAL TRAVELS, SHOP NO. 3, GROUND FLOOR, MAYFLOWER BUILDING, OPP. MUKUND LTD., KAMANI, L.B.S. MARG, MUMBAI400 070, INDIA. The following specification particularly describes the invention and the manner in which it is to be performed. 3 Field of invention: This invention relates to a method for optimizing any process production parameter, like production rate or reaction yield, within a chemical processing plant in concern with the process parameters in any preceding section provided that the latter has an effect on the former. This invention also relates to a method for optimizing the rate of consumption of input material and heat energy usage within a chemical processing plant in concert with the rate of production of output material to minimize input material and energy cost in terms of heat energy and more particularly, to a method for producing product wherein the rate of energy consumption is optimized to satisfy a given rate of product production at minimum energy cost over a predetermined time horizon during which time the cost of energy varies Background of invention Not all chemical process parameters can be derived using equations, especially those involving complex chemical reactions. Using this method, process parameters can be made and used to optimize the processes with least computational rigorousness. Preliminary studies of the process were carried out in an agrochemical plant where it gave positive results. The same method was applied to another stage in the process, which also gave positive results. Objects of the invention Following are the objects of the Invention : 1. Identification of processes parameters can be optimized 2. Collection of relevant plant data 3. Selection of appropriate parameters 4. Fitting optimized parameters into mathematical expression. 5. Selection of constraints, if any 6. Input  any standard optimizer 7. Output the optimized value Drawbacks of the existing process: Process industry is characterized by low volume, high value products manufactured in limited development time. All chemical processes can also be optimized to reduce the cost of production and increase profit. But high development costs usual limits the further optimization of processes. Process optimization has been a long time issue of study and many researches have been done in this area. Most of this study involves complex algorithms and models to solve the problem. Most of the chemical industrial processes can be optimized with the use of simple common sense. Process optimization is of two types : 1. Achieved by changes in process parameters  done by a chemical engineer, 2. Achieved by change in process itself  done by a chemist. Usually due to unsteady nature of market conditions, it is not possible to dedicate much time and money on the latter type of optimization. Hence the onus of improving the process falls on the chemical engineer, who also has to justify the resources used for improving the process. This case study showcases such a situation where the organization was reluctant to spend more on a product, which was about 15 years in production and fetched lower and lower profit margins year after year. This demanded process improvements to reduce the product price. Lab scale improvement work has long been ceased and the only way to improve on the process was in the plant directly. The product fetched great demand and thus any change, which affects the production rate, was also not possible. This provides an ideal situation for the concepts outlined in this paper  a commonsensical and cost effective method to optimizing processes. In this case study the temperature and time are the two parameters that played a significant role in deciding the yield of the intermediate product. Since the feeding temperature was highly optimized but the maintaining temperature was not. It was observed that the lower the temperature of maintaining higher was the reaction yield but higher batch time and hence lower productivity. High temperature feeding was tried to reduce time cycle but resulted in lower yield hence a method was to be designed on these constraints. The data obtained for doing the optimization was obtained when the plant was in shutdown stage. Only certain sections of the plant were under shutdown and thus the production was slowed down enabling the extraction of plant data by varying parameters. The actual optimization was done using three methods  two commercially available software as well an inhouse developed algorithm, all of which yielded same results, which proves the effectiveness of the process. This also shows the generality of the process, in view of which suggestions were given on extending it to similar industrial problems. The invention relating to optimization of process parameters can be best explained by following example, which is an illustration of the method of optimization of process parameters Example I: The conversion of TBN (tetra chloride butyro nitrile ) to TBA ( tetra chloro butyric acid) is a hydrolysis reaction done using H+ ions . The first stage of hydrolysis is the conversion of TBN to TBAmide, which is then converted to TBA. TBN » TBAmide » TBA The TBA reaction is the second in the 7 steps for the manufacture of an agrochemical Various factors have been found out as the influencing factors for 2CB yield, provided that the feeding parameters remain the same, 1. TBA maintaining temperature 2. TBAC and TEA purity 3. Presence of water in reactants or in the reaction mass 4. Presence of impurities 5. Final TBAmide content in the TBA reaction mass Chemical Engineer's Point of View As mentioned earlier the Process optimization can be achieved by changes in process parameters  done by a chemical engineer, Here two parameters that were significantly playing a role in the yield were temperature and time. We have tried to exploit and maximize this timetemperature relationship using our method and optimize the yield. The results obtained shows the effectiveness and the generality of the process optimization method that can be extended to similar industrial problems. Intention: To optimize the time cycle of TBA reaction so as to get the maximum yield at 2CB stage. Basic chemistry and chemical reaction The conversion of TBN (tetra chloride butyro nitrile ) to TBA ( tetra chloro butyric acid ) is a hydrolysis reaction done using H+ ions . The first stage of hydrolysis is the conversion of TBN to TBAmide, which is then converted to TBA. TBN * TBAmide > TBA The TBA reaction is the second in the 7 steps for the manufacture of an agrochemical. Reaction conditions The reaction is done is a glass lined vessel . Around 324 kilo moles of H+ ions is taken ( as HCI and makeup H2SO4 ) and TBN fed over a period of about 5 hours at a temperature of ±1°F . The batch size is 18 kilo moles of TBN and total H+ ions correspond to 1.8 kilo moles per mole of TBN. The reaction is exothermic at all temperatures and does not need heating once initiated .In fact, constant cooling shocks are required for temperature control. After the feeding is over, the reaction mass contains TBN, TBA, and TBAmide, the proportion of which is unknown since it is not measured. After the feeding, it is maintained for a minimum of 1.5 hours at the same temperature (149±1°F) and is sampled for TBN and TBAmide content. The usual average content is around 0.2 % by weight of TBN and 35% by weight of TBAmide. The temperature is then raised to a value higher than 149°F so as to reduce the TBAmide content to around 0.2% by weight, which is the maximum possible conversion since the rest will be impurities. Significance of temperature The TBN to TBAmide reaction is an addition reaction while the TBAmide to TBA reaction is a standard hydrolysis reaction. The feeding temperature of 149±1°F is a highly optimized value where as the maintaining temperature is NOT. It has been observed that the lower the temperature of maintaining higher is the reaction yield at the 2CB stage, the 4th stage in the entire process. As a result of experiments carried out in the plant, it was seen that if the reaction mass is maintained at the same temperature of feeding (149±1°F) for longer time (around 1012 hours) till the TBAmide content is less than 0.2% by weight, then the 2CB content is the highest, around 2645.55Lbs, but with higher batch time and hence lower productivity. The exact reason for this is unknown, but is believed that the lower the temperature of hydrolysis, lower the leaching of impurities into the organic layer and hence higher the quality of TBA. This was discovered by accident when high temperature feeding (105±1°F) was carried out in the TBA process. The time cycle was greatly reduced but the 2CB yields were lower. Premise for optimization TBA stage has long been the bottleneck in the production process. The standard time cycle breakup is given as: HCI receiving in reactor 0.5 Hours H2SO4 makeup for normality 0.25 Hours Heating to feeding temperature 0.75 Hours Feeding 5 Hours Temperature raising & maintaining 12 Hours Work up and transfer 4 Hours Total : 23.5 Hours As seen from the table, temperature marinating takes up the largest time (51% ) and hence offers the scope for optimization . As practiced in the plant, we maintain for 1.5 hours at the feeding temperature and then at 185°F for 3 hours without sampling and then raised to 206.6°F for 3.5 hours minimum and sampled . If the amide content is 0.2% or less, batch is terminated or maintained further till it is achieved. These temperature values of 149°F, 185°F, 206.6°F and time values of 1.5 hours, 3 hours and 3.5 hours are purely judgmental and based on experience. 2CB yield correlation The 2CB process has been the trickiest stage in the entire process since it adds the maximum value to the product. Hence it was always been an area of interest since a small increase in the production rate would bring substantial savings to the cost. Needless to say, the stage was running at the maximum possible production rate and there was hardly any scope for improvement but with capital investment. The 2CB yield value has been more expressed in pounds per standard batch size throughout the paper due to the simplicity of expression. In this process, the TBAC (Tetra chloro butyric acid chloride) and TEA (triethyl amine) are fed over a time period to a mixture of hexane and isobutyiene where the latter is the reactant and the former is the medium. As mentioned earlier the various factors that have been found out as the influencing factors for 2CB yield, provided that the feeding parameters remain the same, 1. TBA maintaining temperature 2. TBAC and TEA purity 3. Presence of water in reactants or in the reaction mass 4. Presence of impurities 5. Final TBAmide content in the TBA reaction mass (Note: The TBA reaction almost always has a 100 % yield) It is not easy to analytically derive an expression for TBA maintaining time and temperature to that of 2CB yield. Hence we resort to the optimization process as outlined below. Step 1: To find a TBA maintaining temperature and 2CB yield correlation. Some arbitrary values were taken, they were assumed but which could fall somewhat close to the actual values if real data were analyzed. TBA MAINTANING TEMPERATURE 2CB YIELD (LBSS) 149°F 206.6T 185°F 1.5 3 3.5 2314.85 12 0 2 2535.32 18 0 1 2645.55 1.5 0 4.5 2248.7 Thus from the table , if the temperature maintaining were done at a temperature of 149°F for 1.5 hours and for 3 hours at 185°F and 206.6°F for 3.5 hours , the 2CB yield for a standard 5.2 pounds batch would be 2314.85 pounds . This is the normally obtained yield. Where as if we maintain for 12 hours at 149°F and raise the temperature to 206.6°F and maintain for 2 hours, we get a yield of 2535.32 pounds at 2CB stage for a standard 5.2 pounds batch size. The following correlations were tried out for connecting the temperature, time and 2CB yield. • Modulus of mean temperature minus original temperature • Temperature / time • Time squared / time • Temperature X weight average time (TWAT) The final correlation (TWAT) was found to be matching. The term weight average time would mean that time is weighted. For example time weight % of 1.5 hours is given by 1.5/(1.5+3+3.5) where the denominator gives the total time maintained. Hence the Temperature X Weight average time for 2314.85 pounds is given by the equation: TWAT for 2314.85 = (149*1.5+ 185*3+206.6*3.5)/(1.5+3+3.5) *Eqn. 1 This value as compared to the 2CB yields was found to be inversely related, as is evident from the following table. YIELD 149°F 185°F 206.6°F TIME WEIGHT % TWAT 2248.7 1.5 0 4.5 0.25 0 0.75 192.2 2314.85 1.5 3 3.5 0.188 0.375 0.438 187.7 2535.32 12 0 2 0.1857 0 0.143 157.2286 2645.55 18 0 1 0.947 0 0.053 152.0316 It is to be noted here that TWAT has no physical significance but perfectly fits our purpose of optimization. Step II: To find the equation involving the yield and temperature: Linear fit as well as quadratic curve fits were done using regression analysis tool in MINITAB software which returned the following functions, Linear regression YIELD TWAT CURVE FIT Residual 2248.7 192.2 192.7656654 0.56567 2314.85 187.7 185.5316337 2.168366 2535.32 157.2286 161.4214754 4.1929 2645.55 152.0316 149.3669431 2.664636 Yield = 8.91896*TWAT+3972.75 Quadratic regression YIELD TWAT CURVE FIT Residual 2248.7 192.2 193.4769595 1.27696 2314.85 187.7 183.7486858 3.951314 2535.32 157.2286 158.4661144 1.23754 2645.55 152.0316 149.9445979 2.086981 TWAT=0.00( )113*Yield ^20.662745' 'Yield+1112.39 Step III: To find the kinetic rate constant of reaction : This value was needed since the percentage of conversion after specific periods of time need to be calculated. Thus all the previous data was culled out and analyzed. The data analyzed was for two temperatures, for 149°F and for 80°F. Temperature = 149°F HRS AMIDE CONTENT VOLUME (LITER) V0/V TTO !n(V0/V) RATE CONSTANT(k) % 0 47 1167.48 1 0 0 *** 2 37 919.08 1.27027 2 0.23923 0.119614845 6 23 571.32 2.043478 6 0.714653 0.119108898 AVG k: 0119362 HOUR1 Temperature 176°F HRS AMIDE CONTENT % VOLUME (LITER) V0/V TT0 ln(V0/V) RATE CONSTANT(k) Batch I 1.5 35.9 891.756 1 0 **** **** 5 6.1 151.524 5.885246 5 1.772449 0.35448206.605 9 2.1 52.164 17.09524 9 2.8388 0.315422217 13 0.7 17.388 51.28571 13 3.937412 0.302877865 17 0.3 7.452 119.6667 17 4.78471 0.281453535 Batch II 1.5 39.8 988.632 1 0 0 **** 3 10.1 250.884 3.940594 3 1.371331 0.457110496 6 3.2 79.488 12.4375 6 2.520716 0.42011935 9 1.7 42.228 23.41176 9 3.153239 0.350359851 12 0.7 17.388 56.85714 12 4.040542 0.336711821 15 0.3 7.452 132.6667 15 4.88784 0.325855981 Integral method of analysis was used assuming that the reaction from TBAmide to TBA is first degree with respect to the concentration of TBAmide. This in actual case will have to be verified using the differential method of analysis. Given the values at 149and 185°F, the values at other temperatures were found out using Arrhenius equation, ARRHENIUS EQUATION FOR RATE CONSTANT IS , k = k0 e KAT149TIS 0.119 HR1 KAT80°FIS 0.346 HR1 SUBSTITUTING THESE VALUES, Me = e(E/R(1/T11/T2)) OR Infki/ka) = E/R(1/Tr1/T2) In (0.119) = 2.129 ln(0.119/0.346) = E/R(1/1491/80) OR E/R =440.062 i.e In (k2) = 2.129 + 440.0623(1/1491/T2) .Using this equation , the other rate constants at various temperatures were calculated . Temperature (°F) Rate constant (hour1) 149 0.119 185 0.478 206.6 0.907 The rate constants at 149°F, 185°F and 206.6°F were used for optimization. Step IV: Selection of constraints: Optimizing the time cycle automatically meant that the yield was also to be optimized. The other constraints selected were, Parameter Max/Min value Basis of calculation Conversion at 149°F 35% (minimum) K value calculated at 149°F Final conversion 0.2% (minimum) K value calculated at 149°F, 185°F, 206.6T 2CB yield 2314.85 LBs (minimum) Yield, temperature correlation equation Total maintaining time 8 Hrs (maximum) *** 1. Conversion at 149°F  This had to have a minimum value of 35% 2. Final conversion  Had to be less than 0.2%. 3. Total maintaining time  Had to be less than 8 hours. 4. 2CB yield  Had to be greater than 2314.85 pounds (This is the value to be maximized) The optimization process: Optimization was done using two tools, 1. SOLVER  Microsoft Excel addin. 2. Lingo  version 8.0. Lingo uses sequential linear programming (SLP) for optimization. Step V: Optimization: Using Lingo The global solver in Lingo was used for optimization; the values used in lingo were, The values returned by solver are: 149°F 185°F 206.6°F Original value 1.5 Hrs 3.0 Hrs 3.5 Hrs Optimized value 1 Hrs 13 minutes 6 Hrs 36 minutes 11 minutes The other parameters are , Original value Derived from optimized value 2CB yield 2314.85 2367.112 Conversion at 149°F (v/v) 35% 18.09% Total conversion 0.2 % 0.2 % Total time 8 Hrs 8 Hrs Lingo input: [Objectivefunction] Min=t1 +t2+t3; [Constraints] 1167.48*eA(0.119*t1) 1167.48*eA(0.119*t10.578*t20.907*t3) 3972.758.91896*((149*t1 +185*t2+206.6*t3)/(t1 +t2+t3))>=1059; t1+t2+t3=8; [variables] t1>0 t2>0 t3>0 Lingo output: Local optimal solution found at iteration: 44 Objective value: 8.000000 Variable Value Reduced Cost T1 1.214587 0.000000 T2 6.603619 0.000000 T3 0.1817944 0.000000 E 0.1000000E+08 0.000000 Row Slack or Surplus Dual Price OBJECTIVEFUNCTION 8.000000 1.000000 CONSTRAINTS 755.7685 0.000000 3 49.68000 0.000000 4 1308.112 0.000000 5 0.000000 1.000000 VARIABLES 1.214587 0.000000 7 6.603619 0.000000 8 0.1817944 0.000000 Savings and advantages: Neglecting the energy savings, the total savings per annum is calculated as follows. Yield improvement per batch of 2CB = 19.85 Lbs. Average raw material cost of 2CB = 150 Rs Average number of batches of 2CB per month = 200 Thus savings per year assuming 11 month year = 29,70000 Rs Hence there is a saving of about $ 67500 (at the rate per year if the results were implemented. Initial cost and capital expenditure: There is absolutely NO EXPENDITURE involved in implementing the suggestions. The process merely involves changing the duration of temperature maintaining as suggested by the computer .No purchase of costly machinery or equipment is necessary. The same procedure can be tried out on a trial and error basis in the plant. But this would involve much time and may seriously affect the yields if the results were negative. This optimization as done in the computer removes all risks and provides with a fair degree of flexibility for the process. The optimization program could predict with good accuracy the different parameters provided the assumed method is rigorous enough. Results and suggestions: The optimized values could be practically tried out in the plant to improve upon the reaction yields. As suggested by the optimizer, there could be an improvement of 19.85 pounds of 2CB yield and an annual saving of 30 lacs if the value were tried out. Further scope of work: As given before, the feeding/maintaining temperature of 149°F is a highly optimized one, as derived by lab work. Whereas the temperature of 185°F and 206.6°F are NOT. Thus the next step of work would involve the optimization of these temperatures subjected to constraints. This process would involve the simultaneous optimization of temperature and time values. Sources of error and approximations: Parameter Error/Approximation Scope of rectification 2CB yieldTBA temp correlation Inherent error in approximation More rigorous method Reaction kinetics Assumed to be first order Actual data Corollary: Instead of maximizing the yield keeping the time cycle constant , the time cycle was minimized keeping yield constant. The result is as given below. Original value Derived from optimized value 2CB yield 2314.85 2314.85 Conversion at 149°F 35% 19.05% Total conversion 0.2 % 0.2 % Total time 8Hrs 7 Hrs 34 minutes The values returned by solver are: 149°F 185°F 206.6°F Original value 1.5 3.0 3.5 Optimized value 2 Hrs 33 minutes 0.0 Hrs 5 Hrs 1 minute Savings and advantages: Considering this saving of 26 minutes over a batch, the total saving per year is calculated as follows. Time saved per batch = 26 Minutes Number of batches produced per month = 60 Total time saved per month = 60*26*.95 (considering a down time of 5°/i = 24.7 Hours Time cycle for one batch = 23.5 Hours. Thus we can produce on extra batch if the result were implemented. Example II: Optimization of batch cleaning time A chemical reaction usually produces a tarry waste, which is found to reduce the reaction yield. There are no direct methods to remove this waste, which sticks to the vessel walls and impeller shaft. Presently after completing a certain number of batches, we open the reactor, spray it with a solvent and remove the waste. But this process takes up time even though it increases reaction yield. Hence not all batches can be cleaned. In plant we have fixed that 1 in every 5 batch will be cleaned. This batch is denoted as CL (for Cleaning) and the forth coming batches as NC1 (NonCleaning 1) NC2 (NonCleaning 2) depending on it's precedence after the CL batch. The selection of this number ('5' in this case) is extremely difficult and hence we follow a method based on intuition rather than real optimization. Such a situation caters for a better balance between yield and time cycle. SCOPE OF OPTIMIZATION: This optimization does not envisage any process modification or change. It only tries to find out how we can arrive at a better system by optimum usage of resources by the application of method. Instead of doing all this, if we could make an arrangement to clean the reactor after every batch with minimum down time, then that, no doubt will be a better option. But this work does not involve any capital investment at all. Application of method, Here the parameters to be optimized are, 1. Batch cleaning frequency So as to maximize reaction yield The method applied is, Number of batches (N) times yield of one batch. The plant data for March 2003 is as follows, Type of batch Yield in KGS No. of batches NC1 1100 KGS 38 NC2 1105 KGS 39 NC3 1109 KGS 38 NC4 1114 KGS 39 CL 1121 KGS 39 Several mathematical manipulations are required since we have to convert the method parameters (yield and number of batches) into the parameter to be optimized (Number of batches) BASIS FOR CALCULATION AND TERMS USED: BASIS: One month of operation The following terms need to be defined for optimization: Nn = Number of noncleaning batches. Yn = Yield of noncleaning batch. Nc = Number of cleaning batches. Yc = Yield of cleaning batch. Tn = Time cycle for non cleaning batch Tc = Time cycle for cleaning batch We know that noncleaning batch is not a single one, but a combination of 4 non cleaning batches. Hence, the yield for a noncleaning batch also will be a variable one. Time cycle, however remains the same. Before applying the this method , some manipulations will have to be done. For one month, the total production in kilograms (denoted as P) would be, P= NnYn + NcYc But, Nn = (x1)Nc; Where x= 1,2,3 no of cleanings eqn. (a) Substituting eqn.(a), we get Total production, P = Nc( Yn(x1) + Yc) Taking time balance, TnNn + TcNc = Tt = Total time But total time, Tt = 24 x 30 x 4; for a 30 day month and 4 reactors. Substituting eqn.(a), we get Nc(Tn(x1) + Tc) = Tt = Total time From experience we know that a cleaning batch takes 16.5 hours average to complete and a non cleaning batch takes 14.5 hours. Substituting these values, Nc{14.5(x1) +16.5} = 2880 hours (1) In addition, we have Nc{Yn(x1) + Yc} = P KGS (2) (2)/(1) => (Yn(x1) + Yc) /(14.5(x1) +16.5) = P/2880 (3) This is the equation to be optimized. Relation of method with process parameters, Yn = Yc  Kx; Where K is the decrement for Xth batch which is cleaned Yc = yield of cleaning batch. Substituting, [(YcKx)(x1) +Yc]/[14.5(x1 )+16.5] = P/2880 or P = 2880 {(Yc  Kx)(x1) + Yc}/{14.5(x1) + 16.5} (3) In this case, the relationship is given by, Yc=1121 Kgs and K = 5 Kgs , i.e. the yield decreases by 5 Kgs for every consecutive non cleaning batch. Substituting the values give, P = 2880(5x +2 ) 1126x)/(14.5x + 2) Optimizer input: Objective function Max P = 2880(5xA2 + 1126x)/(14.5x + 2) [Constraints] {None} [variables] x>0; Optimizer output: The objective function, being a single variable function does not have to be fed in to any standard optimizer to find out the optimum. Finding the Tabulating the value of x and P we obtain the following table, X P 1 195665.5 2 207360 3 210967.9 4 212352 5 212810.7 6 212796.4 7 212507.8 8 212046.1 9 211468.1 10 210808.2 11 210088.4 The graph of production versus batch frequency is depicted as follows, i.e. the peak production occurs at x = 5 or we have to clean once in every 5 batches. RESULT: As practiced in the plant the reactor cleaning after every five batches is found to be mathematically the optimum value by the application of method. Advantages: 1. The method requires no complicated derivations or mathematical modeling. 2. The method can be done with the least number of data points. 3. Any optimizer can be used for the final process optimization and given the optimizer gives global optima, the values have to be same. The number of iterations taken by the optimizer will depend on the efficiency of the algorithm used. 4. The major advantage is that the method is completely independent of the actual reason for the existence of constraints. Example: In example I the actual reason for the relationship between yield, temperature and time may be anything simple or complex. But the heuristic method does not have to take these into consideration. 5. The method can be applied to both single and multi variable processes (Example I is a multivariable case whereas example II is a single variable case) Drawbacks: The following are the drawbacks of the system, 1. The accuracy of the method will depend on the rigorousness of the heuristic model. For example consider example I suppose instead of TWAT model, we use the temperature/time method, The relationship is given as, Yield Temperature/time 1020 66.222 1050 100.714 1150 54.0833 1200 100.722 This method cannot be used since there is no 'one to one' relationship between the model and the corresponding parameter. , there are two The graphical representation is given by, corresponding values for yield. In other words unless all the process parameters which are to be optimized behaves as a function, the process cannot be optimization. 2. The second disadvantage is in fact the method works only if there is a direct influence on one or more process parameter on any other parameter. For example if the 2CB yield does not change with cleaning frequency, it cannot be optimized so also if the 2CB yield does not change with TBA temperature, then also is unoptimizable. Conclusion: The Method based on optimization of parameters was applied to two different and independent chemical process systems and the optimized results obtained. We Claim. 1) A method for optimizing chemical process parameters for producing at least two products from a production site to satisfy a given product demand for each of said products within a range of a temperature with which the rate of product production required to satisfy such demand is correlated to heat energy consumption and varied in accordance with a predetermined schedule of production to minimize the cost of heat energy consumed in a said process, with the cost of such heat energy dependent upon variable parameters comprising the steps of: formulating a process parameters for said production site which characterizes the operating characteristics of the production site as a functional relationship between the rate of production of each of said products from said site, including batch wise in the production of such products, and the amount of energy consumed in the manufacture of each of said products . 2 A method for optimizing chemical process parameters as claimed in claim 1 whereas variable parameters are temperature and time. 3 A method for optimizing chemical process parameters whereas the said products are related to production of agrochemicals. 4 A method for optimizing chemical process parameters comprising the said optimized parameter such as temperature method as substantially herein described and illustrate by example 1. 5 A method for optimizing chemical process parameters comprising the said optimized parameter such as time as substantially explained by example 2. ABSTRACT A method for optimizing chemical process parameters for producing at least two products from a production site to satisfy a given product demand for each of said products within a range of a temperature with which the rate of product production required to satisfy such demand is correlated to heat energy consumption and varied in accordance with a predetermined schedule of production to minimize the cost of heat energy consumed in a said process, with the cost of such heat energy dependent upon variable parameters comprising the steps of: formulating a process parameters for said production site which characterizes [he operating characteristics of the production site as a functional relationship between the rate of production of each of said products from said site, including batch wise in the production of such products, and the amount of energy consumed in the manufacture of each of said products. 

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Patent Number  213144  

Indian Patent Application Number  860/MUM/2005  
PG Journal Number  13/2008  
Publication Date  28Mar2008  
Grant Date  20Dec2007  
Date of Filing  20Jul2005  
Name of Patentee  1) AGRAWAL SANJEEV RADHAKRISHNA 2) BAFNA SHEETAL KANTILAL 3) CHANDRAHASAN PRASANTH 4) SAXENA PRABAL KRISHNAKUMAR  
Applicant Address  AGRAWAL TRAVELS SHOP NO 3 GROUND FLOOR MAYFLOWER BUILDING OPP MUKUND LTD KAMANI L.B.S.MARG MUMBAI 400 070  
Inventors:


PCT International Classification Number  B01JU 19/00  
PCT International Application Number  N/A  
PCT International Filing date  
PCT Conventions:
