=Paper=
{{Paper
|id=Vol-2843/shortpaper29
|storemode=property
|title=Development of Expert Systems for Convective Drying of Vegetables (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2843/shortpaper029.pdf
|volume=Vol-2843
|authors=Sherzod Mamatov,Farkhod Kasimov,Ulugbek Mannanov
}}
==Development of Expert Systems for Convective Drying of Vegetables (short paper)==
https://ceur-ws.org/Vol-2843/shortpaper029.pdf
Development of Expert Systems for Convective Drying of
Vegetables1
Sherzod Mamatov1, Farkhod Kasimov2 and Ulugbek Mannanov2
1
Institute Biology of SDAS, Jinan, China
2
Tashkent State Technical University, Tashkent, Uzbekistan
sherzod_mamatov@mail.ru
Abstract. The work analyzes the development of information systems, using
the example of an expert system. The introduction of expert systems in the dry-
ing process was examined. The outgoing and incoming drying parameters were
studied, a plan was drawn up, and an expert system for drying vegetables was
developed. Based on the selected initial parameters, the conditions of solutions
were compiled to ensure the optimal process, as well as the yield of a quality
product.
Keywords: Expert systems, convective, drying, vegetables, Relative humidity
1 Introduction
The development of information and communication technologies and their
integration with production processes are the basis for the emergence of new areas of
process control. In particular, today artificial intelligence systems are being
introduced from the lowest level of production (technological processes, local control
systems) to the highest (organizational management, marketing issues) and give
effective results. Problems with decision-making in the face of frequent interruptions
in processes and lack of information require high qualifications and sufficient
experience from the manager. Expert systems based on knowledge are a good solution
to problems, especially in cases where there is a shortage of highly qualified
specialists or their services are very expensive [1-4].
An expert system is a system based on the knowledge of highly qualified
specialists with sufficient qualifications and knowledge in a certain area, allowing
them to make the necessary decisions in problem situations or in situations of lack of
information. In this case, a knowledge model is formed on the basis of a knowledge
base collected from specialists. Professional expert systems are somewhat complex;
therefore, linear programming languages cannot be used in their formation. The
algorithms of such systems are also superior to conventional algorithms. This is due
to the fact that professional expert systems have several complex approaches such as
1
Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribu-
tion 4.0 International (CC BY 4.0).
fuzzy logic, probability theory, uncertainty theory, and set theory. But these problems
can be solved using object-oriented programming languages [5].
Nowadays it is difficult to imagine a manufacturing industry without process
automation. Day after day, science is accumulating new work in the field of
automation, programming, advanced technologies and processes.
To increase the level of knowledge of experts, as well as to reduce the human fac-
tor in the industry, it was decided to develop an automated training system. The sys-
tem summarizes existing experience in diagnosing and troubleshooting problems in
the process, working in an interactive mode, contributes to acceleration. The most
widespread and progressive method of accumulating and transferring knowledge is
expert systems [4].
There are a number of installations for drying agricultural products of an industrial
scale, but the convective drying method is very widespread in the territory of Uzbeki-
stan, and there are also no automated control systems developed by new approaches
that qualitatively describe the process and contribute to the selection of optimal oper-
ating parameters.
In order to study the automation of drying processes and the selection of its opti-
mal operating parameters, an expert system was developed [6-7].
2 Methods
In Uzbekistan, they are widely used in vegetable drying plants for drying vegetables,
fruits, cut into small pieces, and boiled cereals. Drying is carried out with air heated in
air heaters. Their peculiarity is that the product is dried in a dense layer.
An important role in convective drying is played by the parameters of the drying
agent: temperature, relative humidity, speed of the drying agent, layer thickness and
its condition. Therefore, convective drying can be intensified by adjusting these pa-
rameters (Table 1).
Drying agent temperature. At the beginning of the drying process, an increase in
the temperature of the drying agent accelerates the drying process. But at the same
time, heat losses increase, which are most significant at the end of drying, when the
material has low moisture content. The maximum permissible temperatures depend on
the type of material and the drying method.
3 Results and Discussion
Air flow rate - affects the drying rate only in the constant speed section (at constant
temperature and relative humidity). The higher the air flow rate, the higher the drying
rate. This influence is noticeable up to an air velocity of 5 m/s. A further increase in
the air flow rate is limited by the fact that the air jet "rips off" small pieces of the
dried material from the drying surface. This air flow property is used in fluidized bed
drying when the air flow velocity is 5-15 m/s. At the end of drying, the air flow rate
does not significantly affect the drying rate. In this section, the speed is not more than
1 m/s.
3
Relative humidity. At a constant temperature and air flow rate, the decrease in the
drying rate in the first stage is directly proportional to the increase in the relative hu-
midity of the air. Then this dependence decreases and increases again at the final
stage of drying. At this point, the dependence of the drying process on the relative
humidity of the air is determined by the value of the equilibrium moisture content,
which corresponds to the residual moisture content of the material being dried.
Based on the selected initial parameters (Figure 1), the conditions of solutions
were compiled to ensure the optimal process, as well as the yield of a quality product.
Table 1. Inbound and outbound process parameters.
ωfinite Тproduct ωagent Тagent ʋagent ωagent
1 1 1 0 reduce stop increase
2 1 0 0 reduce stop increase
3 0 1 1 stop stop stop
4 0 0 1 stop stop stop
5 1 1 1 - - increase
6 -1 -1 1 stop stop stop
7 -1 1 1 stop stop stop
8 1 -1 -1 - - increase
9 1 1 -1 reduce - increase
10 -1 -1 -1 stop stop stop
11 -1 -1 0 stop stop stop
12 -1 0 0 stop - stop
13 0 -1 -1 stop - stop
14 0 0 -1 stop - stop
Air flow rate - affects the drying rate only in the constant speed section (at con-
stant temperature and relative humidity). The higher the air flow rate, the higher the
drying rate. This influence is noticeable up to an air velocity of 5 m/s. A further in-
crease in the air flow rate is limited by the fact that the air jet "rips off" small pieces
of the dried material from the drying surface. This air flow property is used in fluid-
ized bed drying when the air flow velocity is 5-15 m/s. At the end of drying, the air
flow rate does not significantly affect the drying rate. In this section, the speed is not
more than 1 m/s.
Relative humidity. At a constant temperature and air flow rate, the decrease in the
drying rate in the first stage is directly proportional to the increase in the relative hu-
midity of the air. Then this dependence decreases and increases again at the final
stage of drying. At this point, the dependence of the drying process on the relative
humidity of the air is determined by the value of the equilibrium moisture content,
which corresponds to the residual moisture content of the material being dried.
Based on the selected initial parameters (Figure 1), the conditions of solutions
were compiled to ensure the optimal process, as well as the yield of a quality product.
At the same time, our drying facility is a carrot product, the moisture content of
which has the input and output parameters that are key in the process. Compiled a list
of conditions with a solution to ensure an optimal process. Basically, the moisture
content of the product after the drying process should be less than 14% (this is a
standard requirement):
─ if the moisture content of the product during drying is higher than 14%, it is
necessary to increase the drying time and reduce the temperature of the drying
agent to 50oC;
─ if the moisture content of the product at the outlet falls below 14%, the temperature
and speed of the drying agent must be stopped;
─ the temperature of the product at the outlet should not exceed Tobj≤60 oC, if it is
exceeded, it is necessary to reduce the temperature of the drying agent (up to 50-60
o
C) and the speed (1.5-2 m/s);
─ if the product outlet temperature is below 60oC, the outlet humidity is above 14%,
in this case it is necessary to increase the drying time up to 12 hours, keeping the
drying agent speed 3-4 m/s;
─ if the relative humidity of the drying agent at the outlet exceeds 15 ÷ 20%, the
temperature of the drying agent at the outlet should be reduced;
─ if the drying time increases (i.e., up to 10-12 hours), it is necessary to reduce the
rate of agent drying and the drying temperature
─ when the moisture content in the product is less than 20% at the end of the drying
process, the temperature (50-55oC) and speed (1.5-2 m/s) of the drying agent
decrease (Figure 2).
Fig. 1. Inbound and outbound process parameters.
Fig. 2. Expert for convection drying of vegetables.
After that, the program itself chooses the optimal solutions based on the data.
5
Figure 2 shows an expert system, which, based on the input parameters, will make
error-free optimal decisions.
4 Conclusion
This means that a control center based on an expert system is different from
traditional control centers. Traditional control programs are usually written to
controller memory, where only linear, iterative, or branching algorithms can be used.
The control program based on expert programs is an object-oriented program that
contains various complex algorithms and is stored in the memory of the control
computer. Moreover, these systems will improve over time.
In fact, we have developed the simplest expert system for carrot drying processes.
Based on the influencing factors, it is possible to make the same expert system for
various drying methods (sublimation, microwave, etc.). The expert system is very
simple and makes error-free decisions and also simplifies the work process.
5 Acknowledgements
This scientific work was supported by project 2018YFC0311206 from the National
Key Research and Development Program of China, Institute Biology, Jinan.
References
1. Lin T. M., Durance T. D., Scaman C. H.: Characterization of vacuum microwave, air and
freeze dried carrot slices. Food Res. Int. (1998).
2. Mamatov, S., Zhang, M., Jia, A., Liu, X., Liu, C.: Effect of infrared radiation (IR) pre-
treatment on the freeze-drying of sea cucumber. Int. J. Innov. Technol. Explor. Eng.
(2019).
3. Zamen M., Baghban A., Pourkiaei S.M.,. Ahmadi M.H.: “Optimization methods using
artificial intelligence algorithms to estimate thermal efficiency of PV/T system,” Energy
Sci. Eng., (2019).
4. Protasiewicz J. et al.: A recommender system of reviewers and experts in reviewing prob-
lems. Knowledge-Based Syst. (2016).
5. Jensen B.A., Joseph B., Lipták B.G.: Expert systems. Instrument Engineers Handbook,
Fourth Edition: Process Control and Optimization (2005).
6. Kadirov U., Mannanov U., Aripov M., Meliboyev M., Mamatov S.: Improvement of dill
freeze-drying technology. E3S Web of Conferences, 03002 (2020).
7. Aripov M., Mamatov S., Turobjonov S.: Studying the application of dielectric
pretreatment before drying. E3S Web of Conferences, 01067 (2020).
8. Lakomiak A., Zhichkin K.A.: Photovoltaics in horticulture as an opportunity to reduce
operating costs. A case study in Poland. Journal of Physics: Conference Series, 1399,
044088 (2019).
9. Nikiforov A., Kuchumov A., Terentev S., Karamulina I., Romanova I., Glushakov S.:
Neural network method as means of processing experimental data on grain crop yields.
E3S Web of Conferences, 161, 01031 (2020).
10. Gnezdova J.V., Semchenkova S.V., Kuchumov A.V.: State Backing of Modernization of
Main Funds of Agricultural Purpose within a Matter of Increasing Food Security of Rus-
sia. IOP Conference Series: Earth and Environmental Science, 459(6), 062061 (2020).
11. Terentyev S.E., Gnezdova J.V., Semchenkova S.V. Features of Machine-Technological
Stations Organization in the System of Agro-Industrial Production. IOP Conference Se-
ries: Earth and Environmental Science, 459(6), 062060 (2020).
12. Romanova Ju.A., Morkovkin D.E., Romanova Ir.N., Artamonova K.A., Gibadullin A.A.:
Formation of a digital agricultural development system. IOP Conference Series: Earth and
Environmental Science, 548, 032014 (2020).