A performance evaluation methodology for energy efficient control system alternatives for MIMO systems Текст научной статьи по специальности «Компьютерные и информационные науки»

Software & Systems Received 08.04.19

DOI: 10.15827/0236-235X.127.486-495 2019, vol. 32, no. 3, pp. 486-495

A performance evaluation methodology for energy efficient control system alternatives for MIMO systems

D.Yu. Muromtsev 1, Dr.Sc. (Engineering), Professor, [email protected] A.N. Gribkov 1, Dr.Sc. (Engineering), Associate Professor, [email protected] V.N. Shamkin 1, Dr.Sc. (Engineering), Associate Professor, [email protected] I.V. Tyurin 1, Ph.D. (Engineering), Associate Professor, [email protected]

1 Tambov State Technical University, Tambov, 392000, Russian Federation

Abstract. The paper presents the methodology for selecting the most optimal alternative of an energy-efficient control system for a complex process system. The proposed methodology is may help to solve structural synthesis problems.

Designing a control system is a set of interrelated operations aimed at achieving a specific outcome. The implementation of such project might involve uncertainties and risks, high costs, many stages and considerable time consumption, the need to have a well-coordinated team of executors, as well as no guarantee that there wiil be the expected outcome. The choice of a project management methodology and a strategy depends on the type of the process system and the project implementation objectives, the nature of uncertainties and risks, the possibility of using information technology and parallel design.

Both project risks and design costs depend on the number of alternatives considered during design stages. Therefore, for project management it is necessary to use design process models that take into account the number of alternatives and their effectiveness at each stage of design work. In general, a design process can be described by a functional model in IDEF0 format supplemented by decision-making nodes.

The method of evaluating the effectiveness of alternatives is based on the method of dynamic variation, which assumes that each design stage has a formed group of various alternatives that begin to be developed in parallel. After each stage, there is an expert evaluation session with the following decision on the significance of different alternatives in a group.

As an example, the paper describes using the dynamic variation method for developing a control system for a six-section precision furnace for heat treatment of thermistor workpieces in the air. From a control point of view, it is a typical MIMO system with complex relations between inlets and zones.

Keywords. energy saving, control system, dynamic variation, alternatives, expert evaluation, functional model, design stages, optimal control, control strategies, risk analysis.

The success of implementing an energy-efficient control system is largely determined by the ability to quickly and efficiently develop projects using a wide range of various methods and tools. They include information technologies of marketing, project and risk management, quality management and parallel project management, computer application, functional and information modeling, creation of intellectual archives of projects and multidisciplinary commands, information protection, project management standards, etc. [1-4].

A control system development project is a sequence of interrelated operations aimed at achieving a specific significant outcome. The specifics of such high-tech projects includes uncertainties and risks, high costs, multi-staging and considerable time consumption, teamwork, lack of guaranteed results, the need to use computer technologies and parallel design methods [5].

Any project considered as a process has a life cycle. The main stages or phases of a project life

cycle include motivation and concept formation, research and development (product planning), design, production, implementation, and completion. The results of the work at one stage are used to perform the next one. After completion of each project phase, key decisions are made.

Project management involves a number of procedures, such as management, planning, distribution and regulation of resources (labor, material, equipment) taking into account various constraints (technology, budget and time) at all stages of the project life cycle [1]. The most important procedure is making key design decisions in formulating goals, forming a team, approving a work plan, project feasibility assessment, etc.

In general, the project management problem includes the following initial data:

- information relating to the initiation (motivation) of a project kickoff;

- design process restrictions (time, resources, etc.);

- basic requirements for the subject (object) of the design;

- available resources for project execution.

In order to solve the problem it is necessary:

- to select the methodology of the project implementation;

- to create a team of workers;

- to carry out design stages making sure that the possibility of obtaining the desired outcome is high enough, or stopping work in a timely manner to avoid unnecessary costs.

The choice of a project management methodology and strategy depends on the system type and project implementation goals, the nature of uncertainties and risks, the possibility of using information technologies and parallel design [6, 7].

Risks and costs are the most important components that must be constantly taken into account at all stages of the project life cycle.

A project risk is usually understood as the probability of not achieving the project objectives and the expected results. The risk depends on a big number of factors due to insufficient information or a random nature of the phenomena that affect the project success. These factors include the instability of an economic and political situation, actions of competitors, errors of production personnel, etc. Project costs take into account all types of activities and resources used in the monetary valuation, they can be determined by the method of functional cost analysis.

Depending on project characteristics and a situation in the company, the following main project management tasks are possible:

1) minimizing the risk while limiting the costs;

2) minimizing costs while limiting the amount of risk;

3) meeting the cost and risk constraints.

Both project risks and design costs depend on the number of alternatives considered at the stages of a project life cycle. The main way to reduce the risk is to increase the number of alternatives, however the costs increase in this case.

Therefore, in project management, it is necessary to use design process models that take into account the number of alternatives at each life cycle stage.

We consider the cost and risk models of a project under the following assumptions:

- the project life cycle includes a pre-design and design stages;

- the number and composition of the options under consideration in the z'-th and j-th stages may

differ, i.e. V = V , i, j = 1, s , z ^ j;

- total project costs can be considered as the sum of costs at individual stages of a life cycle;

- the project risk is estimated by multiplying probabilities of complex events.

If many alternatives Vj are developed at the j-th stage, then total project costs Zq are equal to

^ = zo+I I ^ (u , (1)

j= 1 u; e VJ j= 0

where zo is pre-project costs; Zj(vi) is the cost of the work on the alternative v, at the j-th stage; z^

shows the costs of the expert evaluation on completion of the j-th stage.

When the costs of all j-th stage options are the same, tthen formula (1) has the form

zE = z + zl + ^ (Wj Zj + zj ), where raj is the

num-

j=i

ber of alternatives considered at the j-th stage. In general, cost components ze, j = 1, s depend

on the number of alternatives raj.

Assuming that the events involved in the successful execution of work on the alternatives and stages are independent, we can use the following formula for determining the project risk Qp:

Qp =1 -i1 - ,o )П 1-П ^ ( u, )

j=1

(2)

where qo is the pre-project stage risk; qj(v,) is the risk of alternative v, at the j-th stage.

If the risks at the j-th stage are similar for all the alternatives v, e Vj, then in this case the formula (2) will be written as j

Q, = i-(i - qo )n (i - q?).

j=i

Formulas (1), (2) are the basis of a design process model that takes into account various options during project life cycle stages. These formulas show that if the number of alternatives considered increases, the costs Zq increase proportionally to the number of options. The dependence of risk Qp on the number of alternatives raj at the j-th stage of the project is more complex. When the number of alternatives increases, the project risk decreases in dependence that is close to hyperbolic.

Dynamic variation method

The main idea of the dynamic variation method is as follows [2]. Each design stage has a formed group of diverse alternatives that are developed in parallel. Each stage finishes with the expert evaluation and a decision on the significance of individual alternatives in the group.

In general, the design process can be described by a functional model in IDEF0 format supplemented with decision-making nodes [2, 8]. The scheme of one model node is shown in Fig. 1.

According to Fig. 1, the basis of the modified functional model of a process description at various stages of design consists of nodes from two blocks: an action block (A) and a decision block (Dm), as well as inputs (I), outputs (O), control (C), mechanisms or resources (M), a criterion and a method (Q), experts (S) and solution results (R).

The dynamic variance method is based on the following provisions:

1) several possible alternatives are considered at each project stage;

2) after completing each stage, the group of these alternatives may change;

3) the probability of achieving the desired outcome is considered as the main criterion when comparing alternatives;

4) for each life cycle stage, there are characteristic signs of the formation of alternatives, which can be considered as a principle of system operation, its design, taking into account possible functioning states, etc.;

5) the exclusion of "unsuccessful" alternatives is conditional; if necessary, you can return to them and continue their development;

6) after receiving new information, the initial data of a design problem during a project life cycle is adjusted, and part of the calculations is revised (based on feedback).

Improving the efficiency of design when using the dynamic variation method is achieved by:

- considering several alternatives;

- changing the composition of a group of alternatives according to the results of implementing individual stages;

- carrying out analysis of alternatives and making decisions after each stage;

- using additional information received during the design process, for example, about the characteristics of a similar product from potential competitors;

- revising previously taken decisions based on new information relevant to the project;

- applying a set of particular criteria when comparing alternatives.

The considered method takes into account two aspects of a project dynamics. First, the number and composition of alternatives may vary at each stage. Second, during the design time, various types of parameters related to task formulation and goals formulation can be changed due to the information flow from the external environment, for example, the values of the key components of a design object, their importance, etc.

The application of the dynamic variance method is considered in the following example.

The control system of a precision six-section furnace used for heat treatment of thermistor billets in air is considered as an object of design. From a control point of view, a furnace is a typical MIMO system with complex interrelationships between the input, output, and internal portions of zones. The rationale for the project is high energy consumption, a high rejection rate and relatively low reliability of furnace heating elements.

The modified functional model of the complex of these works is shown in Fig. 2.

Let us consider in detail each design stage, i.e. actions Aj and decision making Dm¡, j = 0,4 in accordance with Fig. 2.

Stage 1.

Block Ao is responsible for implementing a pre-project stage. Based on the available information Jo, the control system design reference point is developed in the form of an array of key project components (KPCs) Krp = (k?, kdp, Kp, kp), where

, kj, , kj are coefficients that take into account the reduction of energy and defect costs (%), the increase in reliability (%), and the payback period of the control system (years).

When designing KPCs, the controls Coi, C02 include technical and regulatory documentation for the furnace and the procedure for the development of KPCs, while the main resources include marketing staff M01 and network resources M02.

Taking into account the uncertainty in the market for finished products, two situations of furnace operation are possible: normal operation hi, i.e. the furnace is loaded for more than 50 % of the calendar time, and functioning h2 at low (< 30 %) workload. These situations are characterized by the following values:

hi : p(hi) = 0,6, Krp(hi) = (4; 6; 5; 2);

h2 : p(h2) = 0,4, Krp(h2) = (5; 8; 5; 2,5).

Q

Action O

(A) 1 1

¡M

Decision making (Dm)

R

S(M)

Fig. 1. A model with a decision-making node

Coi C02 11 Qo 1

J J0 Ao Коп _ Dmo

Ro Ci Q

TT

Moi M02 Soi So2 Ji

Ai Vi Dmi

Ka, KB

Mi

Sii Si:

C2

02

Vi

V2 j

A2 M s Dm2

M21 TTTm23 t M22 S2

V2'

C3 Q3 4

A3 V3 Dm3

î Î î

M3i M32 S3

V3

C4 * J/ Q4 i

A4 V4 Dm4

î î î

M4i M42 S4

Fig. 2. The functional control system design model

J

2

J

3

3

v

J

4

4

Thus, the output of the Ao block contains values Kp(hi),p(hi), i - 1, 2.

Block Dmo (Fig. 2) is designed to make decisions on the continuation of works. This requires an assessment of the probability Po of a successful project implementation. To do this, we calculate the probability Poi of the correct selection of operating indicators, the weights of the components (ci, C2, C3, C4) and the shares dk(h) of arrays Krp(h) that have sufficient grounds for improvement.

These values are determined as averages based on the expert statements (50i) and numerical processing of the results (S02) on a computer according to the evaluation procedure Qo. In our case Po, = 0,95; ci = 0,35; C2 = C4 = 0,3; C3 = 0,05; dk(hi) = ci + C2 + C4 = 0,95; dkh) = ci + C4 = 0,65; dk = dt (h)• p(h) + dk (h2)• p(h2)= 0,83 , and the probability of successful project implementation p = dk • P « 0,79.

Calculation dt(hi) considers that there are prerequisites for achieving values kf, kJ, kr,

while calculation dt(h2) - for krf, kj.

The resulting probability Po = 0,79 (result R0) is quite high and the work should be continued, while the risk is about 2i %.

Stage 2.

The block Ai (Fig. 2) includes developing the concept and forming a set of alternative control systems. It provides input information Ji about models, strategies, and hardware. The output presents a variety of alternatives Vi and the values of the KPCs arrays for two groups of alternatives -Ka and Kb. The technical documentation is considered as controls R0 and Ci, while the main mechanism Mi is represented by the automation service personnel.

According to the results of studying the technological modes of the furnace and the existing automated control system in the form of six automatic temperature control systems in sections, a tree structure has been developed. It forms new control system alternatives (see Fig. 3).

Fig. 3 shows that the set Vi consists of eight options that differ, apart from the type of reengineering (A and B), in the strategies of implementing optimal control (SW - software, PZ - positional with phase coordinate feedback) and hardware (PC -computer, CT - controller).

Branch A of the alternatives subset

A A

(u = a SW PC, u = A SW CT etc.) provides the development of control devices for dynamic

А SW

А SW PC

5

J

А SW CT

Ui

U2

^ Control system ^

I

J

А PZ PC

А PZ CT

U3

U4

B

J

^ q

B SW PC

B SW CT

5

U5

U6

B PZ

B PZ PC

Ù

B PZ CT

D

U7

U8

Fig. 3. The tree of control system alternatives

modes of heating (cooling) the furnace and determining optimal modes that will improve product quality while maintaining the existing automatic control systems. Therefore, alternatives of branch A should be categorized as "soft" reengineering.

Alternatives of branch B provide creating a new optimal control system for heating (cooling) modes of the furnace and temperature stabilization. Such options refer to "hard" reengineering.

It should be noted that the values of KPCs arrays in the form of "triple" assessments - the lower bound (Ki), the most likely value (K ). and the upper bound (Ku) - have a generalized character for the two groups of alternatives Va with "soft" reengineering and Vb with "hard" reengineering and are designated as KA=(^KlA,KA,KuB},

KB={KlB'KB,KuB), respectively.

Block Dmi (Fig. 2) is necessary for expert evaluations of "triple" alternatives (k^. k'.'. k)'ri j,

u g {Va u Vb}. When receiving additional information during the execution of the first stage of work, the probabilities p(h), h e {hi, h2} values Krp(h) can be changed, as well as new situations can be introduced. Thus, the input of the expert evaluation session block receives many alternatives and information Ka, Kb. The control Qi is a decision-making procedure, the resources are the staff (Sii) and the designer's technical workstation (Si2), and the output receives a decision about the variety of alternatives Vi.

Possible outcomes of decision making at this stage are:

- groups of alternatives Va and Vb remain for further consideration if

KA ~ Krp > KB ~ Krp > KA~KB; (3)

- only a set of alternatives V, remains if

KB<K„{h2), kA>kB; (4)

- only a set of alternatives VB remains if

KA<KB; (5)

- groups Va and Vb are rejected to create new alternatives if

3 i e {e, d, r, p}:

r, \ \

- project work is terminated as unpromising if

V i e {e, d, r, p}:

{(k^ih^k^ih^tikl-kl]}^

(7)

A {(^ ) U k"p (A2)) ^ [; |; where kU, ku, are minimum and maximum value

il ' iu '

of the i-th component KA (K"A) or KB (KuB); sign g in (7) shows that all interval values [kU, k" ], u e { Va u Vb} are "worse" than any kf (h), h e {hi, h2}.

Based on values (<, Kv, Kuv), u e {VA, VB},

Krp(h), h e {h\, hi) and relations (3)—(7) experts assign triple risk assessments (qf, qv, <?°) to implement alternatives Va and Vb. These risks are used to calculate overall risks using the formulas:

Q: =[l-{l-Qrp){l-q)\l№°M

QZj =[l-(l -Qrp )(1--?;)]■ 100%; (8)

Qrp=l-Pp-Je[l,u]-,ve{VA,VB}.

The decision is based on the obtained values Qf, Qf,„ Git» Q?> ' Q*« and the results of the work performed at the concept formation stage.

Let condition (3) be satisfied and (qf = 0,02,

^=0,03,^=0,05), (qf = 0,04; =0,05; gf =0,07),

then according to (8):

Qf =1-0,95-0,97 = 0,0785 (7,85%);

Qf,= 6,9%; Qfu= 9,75%;

Qf = 9,75 %; Qf, = 8,8 % ; Qfu =11,65 % .

Based on risk assessment and given that the cost of the next work stage slightly depends on the number of considered alternatives, the decision maker considers it appropriate to continue research if Vi = Va u Vb.

Stage 3.

The purpose of A2 block (Fig. 2) is to carry out a set of research projects to identify the dynamics model, to identify the links between the input and output variables, and to determine the optimal modes. A set of alternatives Vi and information J2 are sent to the input. The control C2 is a method for model identification. Resources M21 are the equipment and instruments for conducting experiments, M22 is a software module for the identification of dynamics models, M23 is personnel. There is the resulting dynamics model M and the formed set of alternatives V2 at the output.

The set of alternatives V2 is the union of two subsets: V2 = Va u Vb.

In addition, we highlight the factors that significantly affect the indicator kd. However, at the same time we have found no factors that have a close relationship with the component k. Therefore, the values Krp(hi), Krph) are revised, the component kr is excluded from the KPCs array. New values Krp(h), h e {hi, h2} are equal

hi :p(hi) = 0,6, Krp(hi) = (5; 8; 2);

h2 : p(h2) = 0,4, Krp(h2) = (6; 10; 2,5).

The composition and values of the array components Kn, KnJ,Knu ,\> e V2 change accordingly.

The decision-making block Dm2 (Fig. 2) is intended for comparative analysis of subsets of alternatives Va, Vb and assessing risk values for them. Here, the input parameters are the dynamics model M and a variety of options V/ , the subsets of alternatives Va and Vb are at the output. The controlling object Q2 presents Pareto-optimization and risk calculation techniques. Resources S2 are a group of experts and decision module software.

Using values (Kvl,kv, Kvu}, u e V2 and

Krp(h), h e {h1, h2} of the Pareto-optimization method [9, 10], the experts form a set F,'" = {VA o VK \ and similarly apply it to the block

Dm\ (8) to assess the risks <2°, ,j e {/, u}, u eV2p , which turned out acceptable for these alternatives.

Thus, according to the results of the expert evaluation, the number of alternatives considered in the next stages does not change.

Stage 4.

Block A3 of the draft design stage (Fig. 2) analyzes the optimal control in order to determine the possible types of optimal control functions and control implementation strategies, and also assesses the magnitude of the energy efficiency effect. A set of alternatives V/ and information J3 are sent at the input of the block A3. At the output of the block, there are control formed algorithms that use a software strategy (SW) and algorithms with a positional strategy (PZ). Control C3 is a methodology for analyzing energy-saving control on a set of operating states, resources M31 and M32 are the developer's computer and service personnel, respectively.

Possible values of the energy performance effect for alternatives u e F/' = V ( U VB are evaluated using special methods of the optimal control theory. The research takes into account possible changes in network voltage and various types of products.

In block Dm3 (Fig. 2), a decision is made at the completion of the draft project. The input of the block receives control algorithms for alternatives V = V/ . At the output there is a formed subset of alternatives Vf. Control Q3 is a method for decision making under uncertainty, resources S3 are a group of experts and decision module software.

In order to make a decision at this stage, wthere is the effectiveness matrix for the main component (i.e. the percentage of energy savings ke). Table 1

lists the average values ke for three functioning states:

- Hi - one product range is produced with a stable network voltage;

- H2 - one type of product is produced under possible voltage fluctuations;

- H3 - there are different types of produced products, which requires a change in temperature modes.

Table 1

Energy performance cost matrix

Alternatives Functioning states Calculated criteria

#1 H2 #3 qp q# qs qw

Va sw ^ Va pz 6 5 7 6 6 5,5 5

Vb sw ^ Vb pz 9 11 10 10 10 9,5 9

The data in Table i are processed by the methods of equal probability (criterion qp), Hurwitz (qn), Shanyavsky (qs) and Wald (maximin) (qw). The calculated values of the criteria (with a weighting factor c = 0,5 for the criteria of Hurwitz and Shanyavsky) are shown on the right side of Table i [ii].

Taking into account that the next stage of technical design requires considerable labor costs, it is necessary to significantly reduce the number of system options. The efficiency matrix (Table i) corresponds to the matrix of missed opportunities (Table 2) for determining the Savage criterion [i2].

Table 2

Matrix of missed opportunities

Alternatives Functioning states Calculated criteria

#1 #2 #3 r i max qsv

Va sw ^ Va pz 3 6 5 6 4

Vb sw ^ Vb pz 0 0 2 2 1

According to the selected criteria, we should consider the most preferable alternatives u e { Vb, swu Vb, pz } (Fig. 3). Thus, the number of project alternatives is reduced to four.

Stage 5.

Block ^4 is the technical design stage (Fig. 2). It is designed to develop algorithmic and software of the automated control system for the alternatives selected at the previous stage. The input of the block receives information and alternatives v; = l s.(r U VB ,,z . The control C4 is the method of

algorithmic and software design; the mechanism M41 is a designer's technical workstation; the resource M42 is the personnel. The output of the

block receives the working documentation on the alternatives U5, U6, U7, us.

This stage includes developing a version of a control system that is suitable for the final implementation. Using the capabilities of any special SCADA system, a full algorithmic support and software is developed for the automated control system alternatives U5-U8.

It should be noted that the alternatives U6 and us, which use a computer, have greater functionality than alternatives U5 and U7 (on the controllers). However, the latter alternatives are cheaper; the payback period is shorter for them. Alternatives U7, us have a slightly higher accuracy of compliance with process regulations.

Block Dm4 of an expert evaluation session (Fig. 2) is designed to select one of the four options for practical implementation. Input information are control algorithms for alternatives U5, U6, U7, us. The output of the block recevies the documentation for the selected option. The control Q4 is based on the decision-making method under partial uncertainty; the resources S4 are the personnel of the expert group and the software of a decision-making module.

In order to make a decision, we used the hierarchical analysis method [13, 14]. The criteria were energy saving (ke), defect rate reduction (kd) and payback (kp). The structure of the hierarchy and the results of intermediate calculations for this case are shown in Fig. 4.

The calculation of the alternatives ranking shows that R(u5) = 0,228, R(u6) = 0,24, R(u7) = = 0,2566 and R(u7) = 0,2754. Thus, the alternative us is chosen as the optimal one as it uses a positional strategy and a technical tool - the controller.

Software implementation

The considered methodology for assessing the effectiveness of options for building an energy-saving management system is used as a part of the expert system of energy-saving management (ESEM) developed in the Tambov State Technical University at the Department of design of radioe-lectronic and microprocessor systems (Fig. 5).

A methodology for constructing hybrid expert systems designed for solving management problems by multidimensional energy-intensive objects is implemented in the ESEM. The core of the expert system is the knowledge base, which contains knowledge in the field of energy-saving management. The knowledge base includes both general knowledge and information about particular

Fig. 4. Hierarchy structure

cases. The knowledge base of the ESEM uses both theoretical methods of optimal energy-saving control and experts' knowledge. Users and experts interact with the ESEM through a user interface. It is also planned to supplement it with the results of the actual operation of the system knowledge base.

In the automated mode, the ESEM solves the direct and inverse problems of energy-saving control. It requires using the methods that allow visualizing the progress and the results obtained for a designer of control systems on the base of a significant reduction in the dimensionality of the arrays of variables and parameters involved in solving problems.

The Design Support modules allow using a wide range of methods for ranking alternatives, pairwise comparisons, Pareto and Bayes-Laplace optimization, game theory, etc. (Fig. 6), as well as attracting experts via the Internet.

Э Принятие оптимальных решений _ |п| х|

Файл Экспертиза База данных Настройки Информация

□ а в с - -Состояние обрабатываемой задачи Файл, связанный с задачей ...... Новый Класс задачи ................... Задача для эффективности Выбранные методы ............... 11, С, ИИ Количество ситуаций ............ 1 Количество вариантов решения ___ 2

■ I I -Г

Fig. 6. Information window about the problem being solved

Conclusion

The considered example shows that the use of the dynamic variance method expands the possibilities of designing control systems for MIMO high-tech systems by redistributing the composition of alternatives at the life cycle stages, making fuller use of incoming information and changing decision-making methods as the uncertainty in design decreases.

щ Экспертная система энергосберегающего управления

- Анализ и синтез оптимального управления - Ваза зноний (архив четверок) • Одностадийные объекты управления - Многостадийные объекты управления î

<(РДИ. ДА). Э. Пз. О» <(А. ДИ. РДИ. ДА). Э. Пр. О» <(А ДИ. РДИ. ДА). Э. Пз. О» Припер - Поддержка проектных решений • Экслер! ныо оценки Ими1ациониоо модолирооанио Идентификация модели объекта упраолония • Опрвлоленио параметров модели Графическая ин1ерпретация данных Информация \А/пЬ модуль экспертной системы О программе Разработчики Заставка 1 В лип ном пунк-тс меню представлен 1 1 про1раммный модуль решения ЗОУ для 1 формализованно мос1авленной ut мчи 1 исследования, выраженной четверкой 1 символов (РДИ, ДА), Э, Пр, О >., де (РДИ. ДА) - двухстадийиыс модели 1 обьекга - Реальный Двойной Иншрагор 1 и Двойное Апериодическое шено. А Э - вид ннничн шруемою ф\ нкцио- Ж нала - laipatu Энергии. Ж Ир - вид стратегии управления -Ж Программная. Ж О • Ограничения на ЗОУ f

X Завершить работу с экспертной системой Выполнить I Ö

Fig. 5. Main program window

Acknowledgements. The study has been financially supported by a grant from the Russian Foundation for Basic Research, projects no. 17-0800457-a and no. 18-0800555-a.

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УДК 519.816 Дата подачи статьи: 08.04.19

Б01: 10.15827/0236-235Х.127.486-495 2019. Т. 32. № 3. С. 486-495

Методика оценки эффективности вариантов построения системы энергосберегающего управления многомерным технологическим объектом

Д.Ю. Муромцев 1, д.т.н., профессор, [email protected]

A.Н. Грибков 1, д.т.н., доцент, [email protected]

B.Н. Шамкин 1, д.т.н., доцент, [email protected] И.В. Тюрин 1, к.т.н., доцент [email protected]

1 Тамбовский государственный технический университет, г. Тамбов, 392000, Россия

Аннотация. В статье представлена методика выбора наиболее оптимального варианта системы энергосберегающего управления сложным технологическим объектом, которую удобно использовать в задачах структурного синтеза.

Проектирование системы управления представляет собой совокупность взаимосвязанных операций, направленных на достижение конкретного результата. Особенностями таких проектов являются наличие неопределенностей и рисков, большие затраты, многоэтапность и значительное время выполнения работ, командный состав исполнителей, невозможность гарантированного получения ожидае-

мого результата. На выбор методологии и стратегии управления проектом оказывают влияние вид объекта и цели выполнения проекта, характер неопределенностей и рисков, возможность использования информационных технологий и параллельного проектирования.

Как риск проекта, так и затраты на проектирование зависят от числа рассматриваемых альтернативных вариантов на стадиях проектирования. Поэтому для управления проектами необходимо использовать модели процесса проектирования, учитывающие число вариантов и их эффективность на каждом этапе проектных работ. В целом процесс проектирования можно описать функциональной моделью в формате IDEF0, дополненной узлами принятия решений.

Основу методики оценки эффективности альтернативных вариантов составляет метод динамической вариантности, суть которого в том, что на каждом этапе проектирования формируется группа разнообразных вариантов, которые начинают разрабатываться параллельно. После каждого этапа производится экспертиза и принимается решение о значимости отдельных вариантов в составе группы.

В качестве примера в статье рассмотрено применение метода динамической вариантности для разработки системы управления прецизионной шестисекционной печью, используемой для термической обработки заготовок терморезисторов в воздушной среде, которая с точки зрения управления является типичным многомерным объектом, имеющим сложные взаимосвязи между входом, выходом и внутренними участками зон.

Ключевые слова: энергосбережение, система управления, динамическая вариантность, альтернативные варианты, экспертные оценки, функциональная модель, стадии проектирования, оптимальное управление, стратегии управления, анализ рисков.

Работа выполнена при поддержке Российского фонда фундаментальных исследований, проекты №№ 17-0800457-а и 18-0800555-a.

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