Characterization, Design and Optimization of Industrial Phosphoric Acid Production Processes

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Characterization, Design and Optimization of Industrial Phosphoric Acid Production Processes by Artificial Neural Network 1 Gholamhosseion Grivani1,a, Shahriyar Ghammamy2,b, Farzane Yousefi1,c, Mehdi Ghammamy3,d 1- Department of Chemistry, Faculty of Science, Damghan University, Semnan, Damghan, Iran 2- Department of Chemistry, Faculty of Science, Imam Khomeini International University, Qazvin, Iran 3- Department of Mechanical Engineering, Faculty of Engineering, Tehran University, Tehran, Iran a- grivani@du.ac.ir b- shghamami@yahoo.com c- f4380110672@gmail.com d- Mghamazi@alumni.ut.ac.ir DOI 10.2412/mmse.00.114.96 provided by Seo4U.link

Keywords: phosphoric acid, optimisation, modelling and simulation, genetic algorithm.

ABSTRACT. In this paper we optimized industrial scale phosphoric acid production processes by genetic algorithm and Artificial Neural Network. In this work an efficient method is suggested to design an optimized cast in order to increase the rate of phosphoric acid and purity percent in manufacturing process. The predicted results are in very good agreement with the experimental data with an error of less than 4.88%. The simulation model has been examined with real experimental data obtained from the Phosphate Mines. A parametric study has been made to find the optimum operating conditions of the pilot plant for a given phosphate rock. The effect of varying reactor(s) time, sulphuric acid rate, water rate, soil rate agitator–impeller speed has been investigated. More than 160 samples were made in laboratory and the results are derived by changing the parameters and using the limited component software. We also hope to gain feedback that will improve the modeling to better meet the needs of the phosphate industry. Finally, using the Pareto front extracted from optimization algorithms, optimum template selection and remodeling results have been examined with finite element software. Studies have confirmed that using the developed method is an effective tool to achieve the proper format in order to restructure and reduce the strain based on improving the convergence rate and the applied force.

Introduction. Phosphoric acid is an important intermediate chemical product. It is added to foods as a preservative, acidifying agent, flavour enhancer, and clarifying agent. Phosphoric acid is also used in processes such as the coagulation of rubber latex, electro polishing, soil stabilization, and as a catalyst in the production of propylene and butene polymers, ethylbenzene, and cumene. Eightly percent of the acid is used in the production of agricultural fertilizers. Production capacity for phosphoric acid yielded about 33 million tons of P2O5. It is mainly obtained through the attack of phosphate rock with sulphuric acid. Its quality depends greatly on the P2O5 rock content and nature of the present impurities, among which some can be recovered like uranium, rare earth, etc. . Almost all phosphoric acid needed for the fertilizer industry is produced by wet processes. In many of these processes, the raw phosphate ore is converted into phosphoric acid and calcium sulphate di-hydrate (gypsum) by adding a mixed solution of sulphuric and phosphoric acids to the reactor. genetic algorithm is one of the random optimizing methods invented in 1995 by Kennedy & Eberhart . multi objective particle swarm algorithm (MOPSO) simultaneously searches different spaces of design and optimizing points for the complex problems such as non-convex and discrete problems. In MOPSO method, selecting the best local guide (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of 1

© 2017 The Authors. Published by Magnolithe GmbH. This is an open access article under the CC BY-NC-ND license http://creativecommons.org/licenses/by-nc-nd/4.0/

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

solutions, especially when optimizing problems with high number of objectives. In this paper an efficient method is suggested to design an optimized cast in order to increase the homogeneity of the microstructure material and to reduce the applied force required in the manufacturing process. In this work, we develop a design framework that is able to transparently capture the process phenomena involved regardless of the modelled process task. Reactions of phosphoric acid production display below [1-8 ]: 3Ca3(PO4)2.CaF2+ 14H2SO4 →10 Ca( H2PO4)2 +2HF↑

(1)

Ca( H2PO4)2+ H2SO4 + nH2O → 2H3PO4+ 10 CaSO4. nH2O

(2)

Chemicals and reagents In laboratory, we mixed different rate of water, soil and Sulphuric acid by different time. After several minutes, reaction completed. Table 1 shows the values of input parameters and their output objective functions.

Table 1. Values of input parameters and their output objective functions.

1

400

40

210

540

425

0.6938

2

350

60

150

480

287

0.64134

3

300

60

180

540

330

0.7087

4

300

60

210

540

359

0.7584

5

450

60

90

360

174

0.6991

6

500

60

60

300

371

0.3372

7

500

60

30

300

297

0.2621

8

450

50

120

360

125

0.6964

9

500

40

120

300

407

0.4697

10

350

70

120

480

275

0.5387

11

300

80

120

540

159

0.5773

12

350

70

90

480

198

0.4653

13

300

80

60

540

88

0.3873

14

300

80

30

540

153

0.309

15

450

50

150

360

317

0.5806

16

500

40

180

300

494

0.5977

17

500

40

210

300

428

0.6583

18

450

50

150

480

370

0.54029

19

500

40

180

540

443

0.608

20

500

40

210

540

480

0.6409

21

350

70

90

360

165

0.461

22

300

80

60

300

101

0.50008

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Modelling with artificial neural network Artificial neural network (ANNs) are non-linear mapping structures based on the function of the human brain. They are powerful tools for modelling, especially when the underlying data relationship is unknown. ANNs can identify and learn correlated patterns between input data sets and corresponding target values. An artificial neuron is a computational model inspired in the natural neurons. Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals received are strong enough (surpass a certain threshold), the neuron is activated and emits a signal though the axon. This signal might be sent to another synapse, and might activate other neurons. Each neuron in our brain accepts input from many other neurons and then provides a resulting output. This is precisely what we will be replicating in code. Each neuron class will have a structure where there is a body of attributes and one output [9-10].

Fig. 1. Neural network training.

Considering four effective parameters in cast design includes water rate, acid rate, soil rate, and time, which have more effects on sulphuric acid rate and purity percent, in order to recognize the first objective function (sulphuric acid rate), artificial neural network is designed with four entrance neuron layers, two middle layer with 12 and 1 neurons and external layer with one neuron.

Furthermore, in order to recognize the second objective function (purity percent), another neural network is designed with four entering neuron layers, middle layers, two middle layer with 14 and 1 neurons and external layer with one neuron. Figures 2 and 3 shows the Modelling of artificial neural network for first and second objective functions

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Fig. 2. Modeling of artificial neural network for first objective function (sulphuric acid rate)

Fig. 3. Modeling of artificial neural network for second objective function (purity percent).

The physical topology of a network refers to the configuration of cables, computers, and other peripherals. Physical topology should not be confused with logical topology which is the method used to pass information between workstations. Logical topology was discussed in the Protocol chapter.

In order to train and test the neural network in order to forecast the homogeneity coefficient and amount of applied force in the cast, precise data are needed, so some models are produced by finite element software and changing the cast design parameters to diagnose the search space related to the neural network and calculate and save the objective functions. Produced data in pervious stage include corner and curving angles, radius ration, friction coefficient, strain homogeneity coefficient and applied force. Around 85 percent of the data are considered for training and others for testing the predictability of the network for new data. Squared error of the network for training is 0.0079 and in testing is 0.0096. Squared errors of the testing shows that the network is not affected by over training and maximum error of the neural network is 4.88 %. Neural network estimated the homogeneity coefficient with 95.12precision. Neural network output is compared to laboratory data in figure 5.

MSEtr =0.0198 MSEts =0.0198 Ans =7

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Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

Fig. 5. (a) Squared error of the network for testing, (b) The output of the artificial neural network (ANN) model for data test.

Training process of the second neural network is done same as the previous network, in order to predict the purity percent needed for the process based on the effective casting parameters. Using the extracted functions from the above neural network, we can begins the optimization process. Created functions by this neural network acts for cost functions in optimization process.

Optimizing the multi-objective problems Multi-objective optimization is a class of problems with solutions that can be evaluated along two or more incomparable or conflicting objectives. Most optimization problems naturally have several objectives to be achieved (normally conflicting with each other), but in order to simplify their solution, they are treated as if they had only one (the remaining objectives are normally handled as constraints). The Multiobjective Optimization Problem (MOP) (also called multicriteria optimization, multiperformance or vector optimization problem) can be defined (in words) as the problem of finding: a vector of decision variables which satisfies constraints and optimizes a vector function whose elements represent the objective functions. These functions form a mathematical description of performance criteria which are usually in conflict with each other. Hence, the term “optimize” means finding such a solution which would give the values of all the objective functions acceptable to the decision maker. These types of problems differ from standard optimization problems in that the end result is not a single “best solution” but rather a set of alternatives, where for each member of the set, no other solution is completely better (the Pareto set). Multi-objective opti mization problems occur in many different real-world domains, such as architecture (stability vs. cost), and automobile design and as such are a very important problem domain. In solving the multi-objective problem, objective functions usually contradict with each other. It means that improving one function results in decline of the other function. so, all the functions cannot be seen in the best mode. In order to optimize all the objective functions at the same time optimized points are used. Improper points are the points that no other point is dominant over them. Proximate algorithms can find good answers (near optimization) in short time for hard optimized problems. Table 2 shows the design variables and their changes domain. This limit is considered as the limit of the problem. The homogeneity coefficient and maximum applied force are considered as object function [11-15].

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Table 2. Design variables and their changes limit.

Time

Sulphuric acid rate

Soil rate

Water rate

}300,540{

}30,210{

}30,80{

}300,500{

Pareto front shows the objective function toward each other. Optimized points can be considered where two objective functions are satisfied. As it was said, horizontal axis is phosphoric acid rate and vertical axis is purity percent. This chart is composed of discrete points each of which shows the optimized points for both objective functions. After repeating the optimizing algorithms, the optimal points (optimized area) are shown as the figure 6. The parts shown in figure 6 indicate a proper limit for choosing the proper optimizing point since both functions in this limit are satisfied and so several proper points are chosen form this limit. Table 3 shows the values of input parameters and their output objective functions.

Fig. 6. Desired range for the optimal point in Pareto.

Table 3. Optimal point extracted from the Pareto frontunctions.

Objective 1

Optimal 413 cast

Objective 2

Time(min)

sulphuric acid rate(ml)

Soil rate(g) Water rate

0.52876

360

68.2

67.5

486

Summary. Genetic Algorithms are a family of computational models inspired by evolution. These algorithms encode a potential solution to a specific problem on a simple chromosome like the one data structure information of Genetic algorithms are often viewed as function optimizers. Although the range and apply recombination operators to these structures so as to preserve critical of problems to which genetic algorithms have been applied is quite broad. During each successive generation, a portion of the existing population is selected to breed a new generation. Individual solutions are MMSE Journal. Open Access www.mmse.xyz

Mechanics, Materials Science & Engineering, July 2017 – ISSN 2412-5954

selected through a fitness-based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. This work proposes a way that facilitates the modelling, design and optimization of phosphoric acid production processes. As we have demonstrated modern process simulators such as genetic algorithm can be of considerable value for the design, operation and management of phosphoric acid production speed. Optimizing methods taken form nature, which are normally expressed by random qualities and start the research performance from several points.

As this is ongoing work, we intend to further investigate the insights generated through the proposed designs from an industrial user perspective as well as to explore more design cases with respect to phosphogypsum utilization.

Acknowledgment We gratefully acknowledge the financial support from the Research Council of Imam Khoemieni International University by Grant No, 751387-91.

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