=Paper=
{{Paper
|id=None
|storemode=property
|title=Principles of Intellectual Control and Classication Optimization in
Conditions of Technological Processes of Beneciation Complexes
|pdfUrl=https://ceur-ws.org/Vol-1356/paper_34.pdf
|volume=Vol-1356
|dblpUrl=https://dblp.org/rec/conf/icteri/KupinS15
}}
==Principles of Intellectual Control and Classication Optimization in
Conditions of Technological Processes of Beneciation Complexes==
https://ceur-ws.org/Vol-1356/paper_34.pdf
Principles of intellectual control and classification
optimization in conditions of technological processes of
beneficiation complexes
Andrey Kupin1 and Anton Senko1
1
Department of Computer Systems and Networks, Faculty of Information Technologies,
Kryviy Rih National University, Partzyizdu str., 11,
50027 Kryviy Rih, Ukraine
kupin@mail.ru, antonysenko@gmail.com
Abstract. These theses contains realization of a typical technological
beneficiation complex for automation of control processes (in the context of
beneficiation of iron ore - magnetite quartzites). The hierarchy scheme of
intelligence control system for such complex combining principles of
neurocontrol, classification and optimal control has been shown. Results of
computer modeling of classification optimization process in the context of
actual indicators of magnetite quartzites concentration have been shown.
Keywords. Intellectual control, classification optimization, beneficiation
technology, iron ore, magnetite quartzites
Key Terms. Intelligence, Control System, Model, Classification
1 Introduction
Nowadays the problem of intellectual control of technological processes is considered
rather actual. Thus necessity of constant improvement of manufacture, increase of
competitiveness, minimization of technological environmental impact demands
application of complex automation systems is based on modern information
technologies (IT) and intelligent control systems (ICS) [1].
Let's consider the complex of technological processes of iron ore beneficiation
(magnetite quartzites). As the object of control such complex is characterized by
sufficient complexity (multichanneling, nonlinearity, non-stationary, illegibility and
incompleteness of information along with great value of transport delay of output
parameters, presence of noise and disturbance, presence of recycles on the majority of
stages, etc.) [2]. Taking into account these properties, statement of a problem and
potential approaches to their decision such complex can be considered as typical [3-
4].
Works of [2-10] are of great importance for the development of intellectual control
theory of beneficiation technology objects. At the same time, despite of considerable
quantity of research and development, existing systems of automation do not always
meet modern requirements and do not provide the effective decision of difficult tasks
in actual conditions in beneficiation process line.
2 Review of existing decisions and task setting
Taking into account multidimensionality, illegibility and incompleteness of
technological information on all levels of control it is necessary to use ICS to support
operators’ (controllers, technologists and other) decision making and increase their
quality [1]. The further task setting of intellectual control of a process line (a section)
can be also conditionally represented by means of classical cybernetics chart "black
box" (Fig. 1). Accordingly, for controlling the beneficiation process set of vectors X,
U, Y, V on the basis of can be formed as follows.
V 1 , 2 , 3 ,..., n
v
g d0
SECTION
C1 C2 C3
I stage II stage III stage
Q0 Q1 Q2 Q3
Internal d1 Internal d2 Internal
variables: βpp 1(β 1) variables: βpp 2(β 2) variables: d3
γ1 γ2
Pm 1 ε1 Pm 2 ε2 Pm3 βpp 3(β 3),βk
Вm 1 ρk1 Вm 2 ρk2 Вм 3 ρk3
ρs1 ρs2 ρs3 γ 3, γ k
В k1 В k2 В k3
ε 3, ε k
Вs 1 Вs 2 Вs 3
βх 1 βх 2 βх 3
βх
Fig. 1. Process line (section) of concentrating as the object of intelligence control
In Fig. 1 such notations are taken: i 1...N r is a number of industrial variety of
ore; Nr is quantity of industrial varieties; i is estimated raw ore grade;
i is specific gravity of every variety of ore; i is an index or a group of
indices that characterize physical and chemical properties of ore (for example, density
of corresponding varieties of ore, strength, grindability, etc.); g g i is index that
characterizes mineralogical and/or morphological properties of ore (for example,
averaged size of magnetite dissemination in ore after varieties); d0 is averaged ore
coarseness before beneficiation; Q0 is an ore consumption on the first stage of
beneficiation; j 1...N s is number of beneficiation stage; Ns – is quantity of stage;
Q Q j , is processing output of each stage; C C j is circulation load; d d j
is averaged product coarseness; is a solid content in pulp;
Pm Pm j
Bm Bm j , Bk Bk j , Bs Bs j are consumption of water to the mill, classifier and
is a pulp density in the process of
magnetic separation respectively; k k j
classification; is a pulp density before magnetic separation;
s p j
pp pp j j is an estimated grade in the industrial product; х х j is loss
of a commercial component in tails; k is a quality of concentrate; j is an
output of useful component in an industrial product; k is an output of useful
component in concentrate; j is an extraction of useful component in an
industrial product; k is an extraction of useful component in a concentrate.
Thus distribution of state vector on input and output indexes is conditional enough
because most parameters on output, for example, of the first stage will be input for the
second, etc.
For further application of multidimensional model such as Fig. 1 (for example, for
decision of identification tasks or synthesis of automated control systems of
beneficiation TP) with using artificial intelligence technology a number of typical
neural network structures that will be offer by the author here.
3 The hierarchy scheme of intelligence control system for such
complex combining principles of neurocontrol, classification and
optimal control
The results of tests of such intelligent systems have proved the possibility of their
application in the beneficiation TP. At the same time, to ensure their operation it is
necessary to determine the values of settings and / or trends in their paths. Further
studies have shown that the determination of the required setting values it is necessary
to carry out by combination of the following [7]:
1. Classification control, that is founded on the basis of permanent accumulation of
technological parameters history database (DB), their grouping on certain signs
(clustering) and determination of value of setting for the measure of similarity to the
current values of vectors: input, output and internal parameters[8, 9].
2. Optimal control, which requires the design of general purpose functionality for
the system and the application of global optimization methods [4, 10].
Main advantages of the classification approach are their potentially high fast-acting
due to the use of well-known methods of clustering and patterns recognition (for
example, neural networks classification). The disadvantage is low accuracy (the
chosen decision is not necessarily optimal, and even quasioptimal). Also, application
of the approach does not always guarantee the result. In particular, this may be due to
such cases:
at the beginning of the system operation, when the database of technological
situations parameters is quite small;
in the case when necessary (similar) combination of parameters (cluster) has not
been met yet in the process of exploitation of ICS;
in changing of flowsheet, regime map, presence of considerable disturbance of
properties of primary raw material (ore, its amount and correlation of mineral
varieties, etc.).
On the one side, optimization approaches in the case of multidimensional goal
function are also characterized by disadvantages that are caused by:
the difficulty of obtaining a sufficiently adequate mathematical model of TP [4],
which is typical for most inertial processes (in particular, the beneficiation);
the bad conditionality of optimization task (presence of great amount of local
extremums) that appears in the case of application of well-known identification
methods of the multidimensional systems (regressive models, Wiener–Hopf
equation, synergetic and self-organizations, artificial neural networks and others in
particular) and greatly limits the application of well-known methods of
multidimensional optimization;
slow convergence rate of computing process during optimization in large number
of cases.
On the other hand, in the case of the possibility of designing the mathematical
model and a good choice of hill climbing algorithm (method) it is possible to solve
control task, which allows to define a really optimal (or quasioptimal) settings, with
certain limitations. Taking into account well-known advantages and disadvantages of
the above-mentioned approaches for the implementation of multichannel ICS of TP of
iron-ore beneficiation the approach based on combination of classification and
optimization algorithms has been offered. Structure of multichannel hierarchical ICS
of TP of beneficiation complex based on the system of coupling of neurocontrol,
classification and optimization methods is shown in Fig. 2.
In Fig. 2 such notations are taken: OCij is a control object (channel), j its number
(j=1,…,ki; ki is an amount of control channels), i is a number of the stage for local TP
(for example, fragmentation, classification, magnetic separation, etc., i=1,…,Ns; Ns is
amount of the stages of beneficiation TP); NCij – intelligence neurocontroller of OCij;
Vij is a vector of disturbing influences for OCij; Yij – a vector of output characteristics
of OCij; Uij is a vector of control influences (actions) of OCij; Xij is a vector of
informative parameters about the state of OCij; Ysij is a vector of settings of output
characteristics of ОCij; TP*i is the complex of all local TP of the certain stage; V*i is a
vector of main influences of disturbing of TP*i; Y*i is a vector of output characteristics
of ТP*i; X*i is a vector of information parameters about current stat of TP* complex i;
Y*si is a vector of tasks (settings) for output characteristics of TP*i; NE*i –
neuroemulator (predictive mathematical model or predictor) for TP of the
corresponding stage.
Three main control levels 1) of local regime parameters (ore and/or water
consumption, pulp density, etc.); 2) quality indices (content of useful component,
output, exception, etc.); 3) complex of TP (fragmentation, classification, magnetic
separation) are divided in the structure.
First stage of beneficiation TP
V11 V*1
U 11 Y11
OC11 Y*1
TP*1
X11
NC11 X*1
Control
Y11, X11, V11, U11 system of
First control channel first stage of
Y s11 beneficiation TP
of iron ore
V1k 1
Y * s1 Y *1
U 1 k1 Y1 k1 NE*1
OC 1 k
1
X 1k 1
N C1k Y1k 1 , X 1k 1 , V1k 1 , U 1k 1
1
Control channel (k1) Y s1k 1
for TP of first stage
Y *s n
Last stage (n) of beneficiation TP
Y *n
Block of optimization of functioning of complex TP ore-concentrating factory
Choice of general Choice of method of
criterion of optimization realization of intellec-
of TP of beneficiation tual control of TP:
- classification;
- optimization.
Fig. 2. The structure of combined multichannel ICS of TP of magnetite quartzites
beneficiation (classification-optimal control)
So, for example, for a complex of TP of the first stage (supposing that for TP of
fragmentation i=1, k1=2): the first channel (OC11) is the correlation of "ore-water"; the
second channel (OC12) is the mill productivity output (at unloading); V11={coarseness
of grading (averaged coarseness) of input product}; V12={physical and chemical and
mechanical properties of ore}; Y11, Y12={ coarseness of grading (averaged
coarseness) of industrial product, productivity after the industrial product, output of
the prepared class}; U11={mill water consumption }; U12={ ore input productivity};
X11={content of solid in the middle of the mill}; X12={all regime indices of mill
work}. Similarly the formalization for other TP of the first stage (classification,
magnetic separation) is carried out. Then the resulting characteristics for a complex of
TP (all stages) as a whole are formed as follows: V*1=V11V12 ( is the operation of
logical combination of vectors); Y*1={quality of industrial product by quality of
useful component, productivity on the output stage}; X*1=X11X12.
The idea of the approach is in application of combined algorithm with combination
of classification and optimal control approaches in order to ensure the acceleration
decision-making process in multichannel ICS of TP of magnetite quartzites
beneficiation. The main features of the implementation of such a system are as
follows [1, 7].
The intellectual analysis of current state of control object is carried out constantly
at the end of the next step of discrete time by the top level of the system on every
stage of beneficiation in the block of optimization of beneficiation complex operation.
The determining of settings (tasks) for the control systems of the corresponding stages
(middle level) is carried out on the basis of a coherent analysis of indexes of all
beneficiation stages. At the same time, in contrast to existing approaches, decision-
making process (definition of the necessary settings) in the system (Fig. 2) can be
occurred through intelligent classification (classification control) or global
optimization (optimal control). Algorithms for the implementation of corresponding
computational procedures will be given in the future.
On the middle level control of TP complex for separate stages is carried out. For
this purpose the level is given the value of optimal settings from a top level and it
determines a task (proves these settings) for the regulators of all local TP and their
corresponding channels of control of every beneficiation stage. From the other side
middle level systems collect primary information about the state of every channel
(control actions, outputs, disturbing) from the subsystems of the bottom level, carry
out its primary processing, prediction of values of input and output indexes of the
stage using of neuroemulator (NE*і). Certain data are also passed on the top level for
decision making and determination of optimal settings for the purpose of the
coordinated control of all stages and complex of beneficiation TP as a whole.
The bottom level of the system controls separate local TP of each stage. For this
purpose the level contains the number of control channels. Each channel has its own
inverse neuroregulator that recreates the inverse dynamics of the process. The task of
work of such regulator is maintenance of necessary value of settings, that is
determined at the top level of the system and given from the corresponding control
subsystem of the certain stage (id est. middle level). In turn, the bottom level
subsystem passes information about the state of each channel (indexes of control
influences, value of output and information signals, disturbance) to the middle level
system at first and then to the top level.
1. Desired Output and Actual Network Output
170
150
130 (U3)
Output (В3, t/h)
110 (U2)
BM3(U1)
90 BK3(U2)
70 BC3(U3)
50 (U1)
30
10
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Exemplar
2. Desired Output and Actual Network Output
240
220
Output (Q3, t/h)
200
180 Q3*
160 Q3
140
120
100
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Exemplar
4. Desired Output and Actual Network Output
67
66,5
66
Output (βK , %)
65,5
65
βK*
64,5
βK
64
63,5
63
62,5
62
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51
Exemplar
Fig. 3. Results of computer modeling of classification optimization process in the
context of actual indicators of magnetite quartzites concentration
4 Conclusions
For the hierarchy scheme of ICS of beneficiation technological complex on the basis
of combination of principles of neurocontrol, intelligence classification and global
optimization contingency approach at forming limit cluster of certain "special
technological situations", that allows to control TP automatically in real-time mode,
determine and propose corresponding control influences has been offered.
The conducted researches, results of computer modeling (Fig. 3) and industrial
tests [1, 5-7] proved that application of neural networks schemes on the basis of
inverse models and neuroemulators as regulators of separate channels of beneficiation
TP has a sufficient dynamics (reasonable time of settings exercise on condition of its
presence), the possibility of the proper disturbance rejection at 10% level and
operation on the conditions of nonlinear limitations (changes of controller parameters)
on the basis of satiation principle. Thus, the task of this work is the verification of
possibilities of classification strategy for reliable determination of optimal values of
current parameters of TP (in the form of the relevant tasks or setting for controllers),
that will provide stable work of local regulators in the above-mentioned terms.
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