Znd. Eng. Chem. Res. 1994,33, 1174-1187
1174
HIDEN: A Hybrid Intelligent System for Synthesizing Highly Controllable Exchanger Networks. Implementation of a Distributed Strategy for Integrating Process Design and Control Y. L. Huang Department of Chemical Engineering, Wayne State University, Detroit, Michigan 48202
L. T. Fan' Department of Chemical Engineering, Kansas State University, Manhattan, Kansas 66506
The development of computer-aided-design systems is the key step toward process design automation. The most difficult phase of this development is to endow the system with the capability to perform conceptual design, Le., process synthesis. This is especially true when a synthesized process is expected to satisfy simultaneously economic and operational criteria. To meet these criteria, it is highly desirable that the first-principles and heuristic knowledge, which can be numerical or symbolic, structured or unstructured, be fully exploited and that the information and data, which can be precise or imprecise, certain or uncertain, be appropriately manipulated. In the present work, a hybrid intelligent design system for synthesizing exchanger networks (HIDEN) is developed by means of a knowledge-based approach, fuzzy logic, and neural networks. This system, built on an artificial intelligence workstation, fully implements the distributed strategy for integrating process and mass exchanger design and control. It is capable of synthesizing heat exchanger networks (HENS) networks (MEN'S) for the recovery of energy and material, respectively. The resulting exchanger networks are cost-effective as well as highly controllable. 1. Introduction
Improved process design has drastically advanced diverse technologies in the process industry during the past two decades. This has led to significant reductions in capital and operating costs in constructing or modifying process plants (Linnhoff et al., 1982; Motard, 1983; Douglas, 1988). The developed design methodologieshave given rise to a variety of computer-aided-designtools. Many of the tools were built using knowledge-based or expert systems technique (see, e.g., Lu and Motard, 1985; Kirkwood, 1987;Baltramini and Motard, 1988;Barnicki and Fair, 1990;Huang and Fan, 1990;Quantrille and Liu, 1991). These tools should greatly facilitate identification of desirable process systems by designers. There has been increasingrecognition among industrial practitioners that the control performance of processes is neglected in the majority of design methodologies and computer-aided-design tools used in the process design. The partitioning of process design and control into two isolated tasks has hindered the development of optimal or even workable control system designs (Shinskey, 1983; McAvoy, 1987;Fisher e t al., 1988;Sheffield, 1992). It is highly desirable, therefore, that these two tasks be consciously integrated. Recently, a distributed strategy has been developed for an active integration of process design and control (Huang and Fan, 1992). This strategy, by means of artificial intelligence (AI) techniques, aims at synthesizing a process that is cost-effective as well as highly controllable. This strategy attempts to endow the exchanger network with controllability starting from the earliest stage of process synthesisin process design. Thisensures that the resultant process structure possessessuperior controllabilityor, more specifically, structural controllability. The present work is concerned with implementing the integration approach by constructing a hybrid intelligent
* Author to whom correspondence should be addressed. 0888-5885/94/ 2633-1174$Q4.50/ 0
system for synthesizing exchanger networks (HIDEN). This system adopts various disciplines of AI, such as knowledge engineering, fuzzy logic, and neural networks. It attempts to fully exploit the knowledge at all levels, including the linguistic, conceptual, epistemological, logical, and physical levels. The system is capable of automatically synthesizing a heat exchanger network (HEN) and a mass exchanger network (MEN) with the lowest total cost and highest degree of structural controllability. 2. Distributed Strategy for Integrating Process Design and Control
Process synthesis is the first and most difficult phase in process design. It consists of three stages: preanalysis, structure invention, and structure evolution (Nishida e t al., 1981). Starting from the earliest stage in process synthesis, structural controllability is imparted to the process of interest by the distributed strategy for the integration of process design and control; Le., structural controllability is to be endowed at the preanalysis stage, enhanced at the structure invention stage, and evaluated at the structure evolution stage. The distributed strategy involves the methodologies for (i)classifying process information,(ii)computing the index of structural controllability to evaluate process performance, (iii) generating the representation and manipulation schemes for enhancing structural controllability, (iv)developing heuristics for attaining the highest possible degree of structural controllability, and (v) implementing a systematic synthesis procedure. These methodologies are described below only briefly. For the details, the reader is referred to our earlier work (Huang and Fan, 1992). 2.1. Classification of Process Information. While the steady-state process data can be precisely specified prior to process synthesis, dynamic data available at the process synthesis phase are usually imprecise and incomplete. Hence, dynamic data should be approximately 0 1994 American Chemical Society
Ind. Eng. Chem. Res., Vol. 33, No. 5, 1994 1175 classified in terms of the intensity of various disturbances, the levels of control precision, and the patterns of disturbance propagation. Degrees of Intensity of Disturbances. The more intense the disturbances of the input variables,the further the output variables deviate from their normal values or set points. The disturbance variables of a process may be lumped into a single variable for quantifying the overall effect of all related disturbances. For instance, for each stream i in a MEN, the disturbance in the source concentration of key component p, in either positive or negative,direction, and the disturbance in the mass flow rate, 6Mi, also in either direction, lead.to a change in the mass flow rate of key component p, 6MP,defined as
(lcliay;; - ahq-)
@ii- Y;i)l}
(1)
On the basis of their magnitudes in the input variables of the exchanger network, the disturbances can be classified into threedegrees: degree-1,degree-2,and degree-3. These degrees can also be linguistically interpreted as slight, moderate, and intense disturbances (Huang and Fan, 1992). This type of interpretation is more acceptable by a designer to understand the rough classification of disturbances. Levels of Control Precision. Usually, it is unnecessary to control all output variables of a process to the same level of precision. For example, the temperature of a stream to a reactor need be controlled very precisely, while that to a dryer need not be; the composition of a highly toxic species in a stream need be controlled very precisely, while that of a nontoxic component in the stream need not be. Heuristically, the control precision of each output variable is divided into three levels: level-l,level-2, and level-3. Similar to the degrees of disturbances, these levels can also be linguistically interpreted as low, moderate, and high precision according to the requirement (Huang and Fan, 1992). Patterns of Disturbance Propagation. Disturbances originating from the inlets of an exchanger network propagate essentially through its downstream paths to its outlets (Linnhoff and Kotjabasakis, 1986). The path length is determined by the number of process units involved in the path. Four patterns of disturbance propagation identified by Huang and Fan (1992) and adopted here are pattern-1 (through 0 or 1process unit), pattern-2 (through 2 process units), pattern-3 (through 3 process units), and pattern-4 (through 4 or more process units). Apparently, pattern-1 provides the shortest propagation path, and thus, the disturbance propagation is very severe. Pattern-4 provides the longest propagation path, and therefore, the disturbance propagation is almost negligible. 2.2. Heuristics. A large number of heuristic rules are available for process synthesis. These rules enable us to attain a variety of goals, such as the minimization of total cost,maximization of structural controllability,and tradeoff between them. Five seta of rules are listed below, the details of which can be found elsewhere (Nishida et al., 1981; Linnhoff et al., 1982; Liu, 1987; Huang and Fan, 1992). (a) Match-pair selection. This set contains four rules for facilitating the selection of feasible matches, which efficiently generates a superior process structure with a reasonably low total cost. (b) Match-end selection. This set contains six rules by which solution space can be drastically reduced.
(c) Match-load selection. This set contains two rules by which the optimal heat or mass load of each exchanger can be determined. (d) Stream splitting. This set contains six rules by which a feasible solution space can be identified and the control performance of a process can be improved. (e) Search acceleration. This set contains two rules by which dead-end search can be effectively avoided and a superior solution can be rapidly identified. 2.3. Indexof Structural Controllability. Thedegree of structural controllability of a process reaches a maximum when the occurrences and severities of undesirable disturbance propagation are at a minimum. Thus, the structural controllability can be assessed by examining the modes or patterns of disturbance propagation through the process. To evaluate the structural controllabilityquantitatively, it is convenient to define the disturbance vector, D, the control precision vector, C, and the disturbance propagation matrix, P, for a process having N streams. D comprises the intensity of disturbances of all process streams and has the form C specifies the levels of control precision required for all output variables; it has the form
c = [CI
(3) A disturbance propagates through one or more disturbance paths and affects the stabilities of output variables. Element p j j in P represents the severity of disturbance propagation from the inlet of stream i to the outlet of stream j in the process. Thus, P has the form c2
CNIT
e..
P=
(4)
On the basis of the vectors, C and D, and the matrix, P, the index of structural controllability, I,c, is introduced in the following general form.
In this expression, EMtt,,(D,C,P) and EMt,h(D,C,P) characterize a process with maximum disturbance propagation and that with minimum disturbance propagation, respectively. For a process containing a multitude of process units including heat exchangers, heaters, coolers, extractors, and absorbers, the index, I,, is written as N
N
N
N
This index has been mathematically proved to have a value in the interval of [O, 11 (Huang and Fan, 1992). When the value of this index reaches 1, the process is completely controllable;however, it may be economically undesirable. When the value reaches 0, the process is completely uncontrollable, which is operationally undesirable. A reasonable synthesis target is to identify a process which
1176 Ind. Eng. Chem. Res., Vol. 33, No. 5, 1994
has an index value as close to 1 as possible and which keeps the cost as low as possible. 2.4. General Synthesis Procedure. The stagewise procedure for process synthesisinvolvesthree major stages, i.e., preanalysis, structure invention, and structure evolution. The major functions of the procedure in each stage are delineated below. The detailed synthesis procedure can be found in Huang (1992). Preanalysis Stage, At this stage, the total cost of the process of interest is estimated by the pinch technology, the input and output variables are identified, and the process data are analyzed and classified. On the basis of the resulting classification, a controllability assessment (CA) table is constructed. The values of certain grids, Le., the values of elements pij's, in the table must be preassigned according to the heuristic rules generated for reaching the goals of lowest possible total cost and highest possible degree of structural controllability. Such a CA table is termed the preassigned CA table, or CA* table. The preassignment of values to these grids signifies the most favorable and the least favorable stream matches. In addition, the synthesis problem should be decomposed into several subproblems, whenever possible. Structure Invention Stage. A series of decisions is made at this stage on the selectionand placement of process units to match pairs of process streams sequentially. The effect of each stream match decision on the structural controllability is assessed by the index of structural controllability. The recommendations for stream matching, reflected in the CA* table, should be adopted. Several sets of heuristic rules for reducing the total cost and improving the structural controllability should be applied to ensure that an optimal solution is indeed obtained. Structure Evolution Stage. At this stage, the structure generated at the previous stage is examined for further improvement in the light of total cost and structural controllability;a trade-off between them needs to be made. This is accomplished by resorting to the heuristic rules specifically developed for this stage. 3. Why Hybrid Intelligent System?
The available information and required knowledge for the design of a superior process can be structured or unstructured, numerical or symbolical, precise or imprecise, complete or incomplete, and certain or uncertain. For instance, energy and mass balance relations are quantitative and numeric in form, but heuristic rules derived from the designer's experienceare qualitative and symbolic in form. Certain process data necessary for the design, such as those reflecting a specified operating condition, are precise, but those related to disturbances and control precision are imprecise. In addition, certain knowledge hidden in a pool of data is extremely difficult to explore. Obviously, the utilization of information and knowledge of all types is the key to the development of an automatic design system of high quality. 3.1. Artificial Intelligence Fundamentals. AI is the structured development of theory and methodology that enable computers to be intelligent (Winston, 1984; Dougherty and Giardina, 1988). The major disciplines of AI include knowledge-based systems, fuzzy logic, and neural networks. A knowledge-based system is a computer program utilizing knowledge and inference procedures to solve problems at an expert level (Rich, 1983). It is especially effective in dealing with both structured and unstructured symbolic knowledge. Fuzzy logic resembles human thought and language much more than conventional logic (Kaufmann, 1977);
HVBWDIN'KLUGENT SYSTEM
Figure 1. Structure of a hybrid intelligent system for the design of exchanger networks (HIDEN).
nevertheless, in compliance with the spirit of logic, it attempts to be precise. Thus, fuzzy logic is, perhaps somewhat paradoxically, a precise system for imprecise reasoning (Zadeh, 1974). It is capable of dealing with imprecisely structured numerical information. A neural network is a computing system comprising a number of highly interconnected processing elements. It processes information by its dynamic state response to external numerical inputs (Rumelhart et al., 1986;Caudill, 1987). Neural network techniques have been widely used to capture unstructured knowledge hidden in a pool of numerical data. 3.2, System Structure. System HIDEN consists of three subsystems: knowledge-based subsystem KBDEN, fuzzy logic subsystem FLDEN, and neural network subsystem NNDEN. The structure of HIDEN is illustrated in Figure 1. Subsystem KBDEN is the core of HIDEN; it accepts the process data pertaining to an exchanger network synthesis problem. The data are divided into two classes: (i)those representing the steady-state operating condition that are precise and (ii) those describing the dynamic operating condition that are imprecise. The first class of data forms the basis on which the pinch point, minimum number of process units, and minimum energy or material consumption are computed. The second class of data is transmitted to subsystem FLDEN. Subsystems FLDEN and NNDEN are designed to provide a series of recommendations on critical stream
Ind. Eng. Chem. Res., Vol. 33, No. 5, 1994 1177
Bembm DIST.CTRLUST [bcdl W.PROB.WCSIE lbcJl TEYP.YCP.US1 CONC.M.UST ENSYNTHESIS
\‘
PR0USS.UNITS
STREAW.MATCHES
moms HEAT.MCHAW6ERS
T.Y.D.UST [ I d ) C.Y.D.UST [local]
‘UASS.EXCHAN6ERS
,
Figure 3. Slob of unit 0PERATION.MATRICES.
WLD.STREANS
Figure 2. Hierarchically structured knowledge base of knowledgebased subsystem KBDEN in system HIDEN.
matching based on the second class of data. These data are fuzzified first in subsystemFLDEN and then computed in subsystem NNDEN. Subsystem NNDEN generates a multitude of the most superior and inferior matching decisions in the light of synthesis criteria. All the derived match decisions are taken into account by subsystem KBDEN, which produces a complete process structure.
4. KBDEN: Knowledge-Based Subsystem Subsystem KBDEN is built on a XEROX 1108 AI workstation using KEE (Knowledge Engineering Environment), a LISP-based, object-oriented, multiparadigm programming environment (IntelliCorp, 1988). This subsystem consists of four components, a knowledge base, a data base, an interface, and an inference engine. 4.1. Knowledge %presentation and Manipulation with Frames and Inheritance. In KEE, knowledge is defined by creating data structures, termed units, to represent objects and concepts of interest. The units are grouped into hierarchies from more general objects, named classes, down to particular objects, called instances. A unit is composed of slots created to represent the attributes of an object. Similar to a conventional data structure, a slot can store numerical and textual data. It can also store additional complex information, including tables, graphical structures, references to other units, and procedural programs. Since a slot can contain programs, a unit can represent the behavior of an object as well as its attributes. The slots in the unit containing behavioral information are called methods. A method may contain a number of functions, each of which performs a specific task. A unit can activate a method in another unit by sendinga message to the method slot (IntelliCorp, 1988). 4.2. Knowledge Base Hierarchy. The knowledge base (KB) has a hierarchical structure as illustrated in Figure 2. In this KB, ENSYNTHESIS is the root unit initiating a synthesis task, This unit contains a number of slots. One of the slots, GLOBAL.SLOT, contains a method for generating a global menu which is part of the user interface of the system. Under root unit ENSYNTHESIS, there are a number of units, some of which have their own subunits. For instance, unit 0PERATION.MATRICES has subunits DISTUR.PROPAG.MATRICES and MATCH.MATR1CES. The slots of unit OPERATION.MATRICES,
displayed in Figure 3, contain two types of slots: member slot and own slot. The member slot is automatically inherited by the slots of the “child” unit;the own slot only belongs to the current slot. This can be illustrated by the following example. T.M.D.LISTS is one of the own slots of unit OPERATION.MATRICES. The slot value is a method which is a program. This program creates two lists: (i) list TEMP.MCP.LIST containing all data representing the steady-state operating condition of a synthesis problem and (ii) list DIST.CTRL.LIST containing all data representing the dynamic operating condition of the same synthesis problem. These two lists are stored in member slots TEMP.MCP.LIST and DIST.CTRL.LIST, both of which can be adopted by subunits MATCH.MATRICES and DISTUR.PROPAG.MATRICES. In the latter subunit, method DIS.PROP.M.MAKING creates a disturbance propagation matrix P for the analysis of the controllability of a process. 4.3. Heuristics. The first-principles knowledge, such as the first and second laws of thermodynamics and mass and energy balances, is precisely represented in mathematical forms and programmed in a method of a slot. Heuristic knowledge, however, is implemented in two forms: IF-THEN rule and heuristic function. For instance, a rule for minimizing capital cost is IF it is feasible to match a pair of process streams in a process unit, THEN eliminate at least one stream after matching. This rule has been implemented in unit MATCH. MATRICES, of which slot MATCH.M.MAKING has a method or program implementing this rule. The outputs of the program are the name of the current end of the problem stored in own slot CURRENT.END.NAME and the stream match matrices of cold-end and hot-end stored in own slots COLD.END.M.MATRIX and HOT.END. M.MATRIX, respectively. A heuristic function is introduced for matching a pair of process streams. This function gives rise to a superior structure with the lowest possible capital cost and less backtracking in searching. Let us assume that the number of heat- or mass-transfer units (HTU’s or MTU’s) needed in an exchanger network is V; thus, the size of the decision set of stream matches, MdW,is U,i.e., (7) Mdec= hl,m2, ..., mu) At step i, decision mi is selected from a set of match candidates mij’s so that
mi = max(mi,l,mi,, ..., mij, ...,mid]
(8)
where J is the number of all thermodynamically feasible match candidates at step i. Each match candidate in this set is evaluated according to the function
1178 Ind. Eng. Chem. Res., Vol. 33, No. 5, 1994
mid =
3
where
HOT.1 HOT.2
HOT.STREAMS
