Energy management technology in pulp, paper, and allied industries

Automatica, Vol. 19, No. 2, pp. II 1-130, 1983

0005-1098/83/020111-20503.00/0 PergamonPressLtd. 1983InternationalFederationof AutomaticControl

Printedin Great Britain.

Survey Paper

Energy Management Technology in Pulp, Paper, and Allied Industries* A. KAYAf and M. A. KEYES, IV:~ A review of multilevel control and optimization methods into process indicates that the operational constraints of process must be closely observed in order to manage the energy efficiently and to provide an acceptable operation with a minimum cost. Key Words--Energy control; computer control; pulp industry; paper industry; power management; optimization; energy management. AI/raet--This paper reviews the state of the art and trends in paper and allied industry energy management. As a structural approach, the plant is first partitioned into unit operations convenient for the definition of multilevel control and optimization schemes: The applicable methods of control and system theory useful for energy management are presented. Problems and solutions at each level are identified and discussed. The process of implementation of energy management functions with computers is included along with a summary list of major energy mangement functions. Furthermore, some important optimization examples are presented and several methods are discussed. Although the paper emphasizes power generation and energy recovery area, the research on total mill energy management is reviewed and the main results are briefly presented.

process design including material selection. Consequently, the effects on measurement and control technology have been profound (Keyes and Kaya, 1979). Increased benefits from the judicious use of energy and feedstocks (wood, petroleum) along with the evolution of lower cost and more powerful computers and distributed microcomputer systems have emphasized the role and necessity of effective energy management in industry today (Kaya, 1978). Furthermore, Uronen (1980) studied the energy selfsufficiencyfor pulp and paper industry and reported the recent developments on the new energy efficient processes in all parts of a pulp and paper process. At the same time, a total energy inventory of many Canadian and Scandinavian pulp and paper plants have been studied (Reside and co-workers, 1981) to determine the area of improvements for improved energy efficiency and operating economy. Reside and co-workers (1981) also reported on the actual energy consumption, per unit production, of each area, within the paper mill for the regions such as eastern Canada, western Canada, and Scandinavia. Some differences in energy consumption between the regions were observed. Fadum (1980) quoted another statistical data for U.S. paper mills as percent energy consumption in each area of a paper mill. This paper presents the current status of energy management applications and technology in the pulp and paper and allied industries. The equipment for energy generation and transmission is common in the majority of industrial plants. Therefore, the methods presented in this paper are of common interest.

INTRODUCTION AND OVERVIEW

THE WASTEFUL USe o f conventional energy r e s o u r c e s must come to an end since the demand for energy has finally extended beyond its availability. This fact is due to four important factors which will dominate the future use of energy:

(a) explosive increases in crude oil pricing, projected to double to at least twice in constant dollar price before the year 2000; (b) increases in the demand of energy to fuel a growing economy along with critical shortages; (c) environmental constraints; and (d) the high probability of violent interruption of imported energy supplies. The above factors have created an energy environment which, in turn, has resulted in major changes in the criteria used for equipment and

* Received 6 July 1981; revised 14 May 1982; revised 19 July 1982. The original version of this paper was presented at the 4th IFAC Conferenc~ on Instrumentation and Automation in Paper, Rubber, Plastics and Polymerization Industries which was held in Ghent, Belgium during June 1980. The published proceedings of this IFAC meeting may be ordered from Pergamon Press Ltd, Headington Hill Hall, Oxford OX3 0BW, U.K. This paper was recommended for publication in revised form by survey paper editor G. Saridis. ~"Mechanical Engin~ring Department, University of Akron, Akron, OH 44325, U.S.A. :~Bailey Controls Co., Wickliffe, OH 44092, U.S.A.

The scope of industrial energy management In general, three kinds of approaches are possible for energy management: (a) product changes toward less energy intensive goods, product mix, or product composition;

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A. KAYA and M. A.

KEYES,

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(b) equipment and process design improvements; and (c) instrumentation, control, and process management improvements.

TABLE 1. POTENTIAL IMPROVEMENTS IN CANADIAN ENERGY DEMAND AND RECOVERYFURNACESTEAMSUPPLY, GJ/t. (RESIDE AND CO-WORKERS,1981)

Item (c) above is generally considered as the domain of energy management. That is, industrial energy management by improved control, instrumentation, and process management for pulp and paper and allied industries is the subject of this paper. Pulp and paper processing currently consumes more energy per ton output than any other major industry. For example, recent research indicated (Hanna and Frederick, 1978) that the U.S. paper industry purchased the equivalent of 3.51 x 1011 kWh in 1973. This represents 10.5% of total energy consumption in the United States with an average specific energy consumption of 6225 kWh/ton of product. A study (Reside and co-workers, 1981) of Canadian and Scandinavian Kraft process requirements revealed a number of technological and operating factors that can reduce a Kraft mill dependence on purchased energy. A model was used to analyze the technical and economic feasibility of several energy saving measures. The result of a case study for a specific mill was presented. The results indicated that the steps necessary to reduce the purchased energy was coincident with general improvement in an overall mill performance. That is, the new improved equipment, process, and control methods can contribute to the improvement of overall energy efficiency. The survey results are presented in Table 1. The projected percent savings due to a computer system are also reported (Uronen, 1980) as shown in Table 2. Recently, many authors (Aarnio, Tarvainen and Tinnis, 1980; Blevins and co-workers, 1979; Beiter, 1977; Kaya, 1978; Methven, 1979; O'Callaghan and Probert, 1977; Ross, 1979; Swanson, 1977) used the words 'energy management' in the title of their papers. The goal of the management process is the stewardship of a vital resource, energy.

Process steam demand 1. Digester 2. Washing and screening 3. Bleach plant 4. Dryer 5. Evaporators 6. Miscellaneous 7. Boiler auxiliaries Kiln demand Electrical demand Process energy demand Steam from organic solids

Survey average

Projected

23.6 3.5 0.8 4.0 4.4 4.2 5.1 1.6 2.8 2.9 29.3 13.7

16.4 3.0 0.1 2.3 3.6 3.5 3.7 0.1 2.2 2.7 21.3 15.0

Partitioning of an industrial plant for enerffy management A natural division in an industrial site has been the manufacturing units (plants) for a specified product. This sort of division for overall plant management may be misleading. It has been found that (Kaya, 1978) a close coordination between energy generation, consumption, and production has to be established. The energy flow in a pulp and paper plant may be divided into three subsystems (Kaya, 1978): (a) energy generation: boilers (including black liquor recovery), electric power generation; (b) energy transmission: pumps, fans, and heaters of energy generation which are driven by steam or electricity as necessary; other auxiliaries for energy recovery, etc.; (c) energy consumption: process area primarily for production (including by-products of energy sources such as black liquor and bark). A coupling (interaction) exists between the production rate and energy availability in terms of reuse of black liquor and bark burning. Similar conditions exist in the petrochemical industry in which

TABLE 2. ENERGY DISTRIBUTION AND SAVINGS BY THE USE OF COMPUTER CONTROL SYSTEMS. (URONEN, 1980) Potential energy savings by the use of computer control Area or process Evaporation Drying Cooking and washing Bleaching Recovery and power plant Others Figures in parentheses are averages.

Energy used (%)

In unit process (%)

In whole mill balance (%)

26 25 18 12 10 9

5-6 5--10 8-30 8-12 3-5 (3-5)

1.5-2 1-2.5 2-5 0.5-1 0.5-1 (1-2)

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Energy management technology in industry by-product fuel gases are energy sources; however, those gases (e.g. ethylene) can be used independently. In the pulp and paper industry black liquor must be burned to recover chemicals for pulp processing. This explains the necessity of an overall plant energy management and coordination strategy.

Theory and methodology Most successful energy management projects currently in operation have started with energy conservation through improved controls and move up to supervisory and coordination levels. A few authors have identified their work in terms of multilevel control and optimization (Kaya, 1978; Womack, 1978; Balchen, 1979; Kociuba and Ponstingl, 1977; Aarnio, Tarvainen and Tinnis, 1980). The functional decomposition employed can be described at three levels.

On-line dedicated controls. These controls perform specific functions and are designed to be responsive to process transients or disturbances. Simply, they control designated variables optimally at the desired set point (set manually or automatically).

Supervisory controls. These functions perform optimizations to coordinate the loads (e.g. set points) of lower-level controls so that they may as a whole provide the most efficient overall operation. It is expected that a dedicated control may provide more efficient performance at the expense of lower efficiency of another one (even though the other is also performing at its best for that load). The general idea here is to determine the set of set points so that the best performance of the aggregate operation is achieved. A critical evaluation of such multilevel (decentralized) controls has been made by Javdan and Richards (1977). Coordination and planning. Higher-level functions such as coordination between optimizing functions, future planning of production levels under postulated alternative energy availability and cost scenarios are performed at this level. The output of this level usually provides constraints and plans which must be satisfied by the lower-level optimizing functions. Kociuba and Ponstingl (1977) for example presented a three-level energy management functional concept as control, supervisory, and planning. Similar to this, a hierarchy of responsibility and activity was proposed by Aarnio, Tarvainen and Tinnis (1980). There is a similarity between the two approaches. However, the difference exists because

one is a structural system hierarchy and the other is administrative. Kaya (1978) proposed three levels of hierarchy as a part of energy management. Above these three levels, a fourth level is proposed as an administrative one, called factory management. There is a general agreement on the characteristics of multilevel controls. These are classified in various ways as shown in Table 3. TABLE 3. FEATURES OF HIGH- AND LOW-LEVEL CONTROL OF SYSTEMS

Low level (controls) Control time interval Control type Control scope Control period Optimization criteria Model

short dedicated (centralized) local immediate time simple criteria detailed dynamical

High level (management) long distributed global future planning multicriteria averaged, static

General features of energy management The general features of energy management includes (however simplistic in their scope): (a) continuous state information on energy conversion, storage, and flow; (b) a model of important plant aspects; (c) input data and constraints; (d) controls and final control element; (e) algorithms, instructions and heuristics; and (f) human interface. A system of these features is complex. The work can be simplified by knowing the structure of the mathematical model, the practical aspects of the specific application considered, and, most importantly, clearly defining the scope and goals of the system prior to design initiation.

Methods of approach and implementation There is much agreement among researchers and practitioners on the appropriate order of implementation of energy management systems. Implementation must start with low-level controls and logically build up to a higher level, leading eventually toward plant-wide energy management. Such stages are often seen in literature (Scott and Bradford, 1980; Reside and co-workers, 1981). Numerous work has been reported on plant applications of industrial energy management (Blevins and co-workers, 1980; Beiter, 1977; Swanson, 1977; Barry and co-workers, 1976). However, some of the work reported followed a systematic and step-by-

A. KAYA and M. A. KEYES,IV

114

step approach to the problem including first, its economic feasibility (Methven, 1979; O'Callaghan and Probert, 1977; Aarnio, Tarvainen and Tinnis, 1980; Kaya, 1978). Methven (1979) listed a number of ingredients of an effective energy management program, and Aarnio, Tarvainen and Tinnis (1980) suggested several steps to be followed in an engineering approach to energy management. The figures of return on investment for each incremental investment are necessary in order to arrive at a sound energy management system. Many times, the user ends up buying a system at once which can be purchased in two steps: (i) a small initial investment with a great return, (ii) an incremental investment with a much lower return. Although the combination of these two is still a sound investment, the user will enjoy immediate (and inherently less risk prone) benefits of a first step rather quickly if the incremental approach is taken. In fact, significant savings can be realized by the enhancement of dedicated (low level) controls and instrumentation alone. This insures debugging, acceptance, and economic justification at each project stage. DESCRIPTION OF METHODS

A complete measurement and instrumentation system should first be available for any proper energy management system. Although measurements and dedicated controls belong to the lowest level of the heirarchy, they are essential for a success-

ful energy management. A schematic of a representative paper mill is given in Fig. 1. Low level controls Combustion controls. Some advances are reported in combustion control of fossil fuel, recovery, and wood-burning boilers (Rashmikant, 1977; Swanson, 1977; Johnson, 1975; Kompass, 1979; Uronen, 1977; Jansen, 1978; Shinn, 1978; Jutila, 1979; Gunsaulus and Johnson, 1977; Nanney, 1976). Jutila (1979) summarized combustion controls in two main categories: O2 sensing and CO sensing. In situ zirconium oxide oxygen probes are used in the majority of oxygen-based systems. The sensor is sensitive to temperature so that either compensation should be made or temperature should be controlled. Oxygen-based control has advantages such as low price, fast response, high accuracy, and generally good stability. The shortcomings are: potential errors contributed by air infiltration, and 02 variations across the stack. Multiple probes are used to eliminate the latter. Mounting the sensors close to the combustion chamber practically eliminates the errors by infiltration; but, depending upon the sensor design they introduce maintenance problems and require special high temperature probes. Gunsaulus and Johnson (1977) have given a detailed control schematic for multifuel combustion control of bark boilers and have also provided schemes that are applicable to black liquor recovery boilers. EN, TRANSMISSIONSYSTEM

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FIG. 1. Energy management system model for paper and pulp plant.

(PROCESS)

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Energy management technology in industry Jutila (1979) also reports that CO sensors are introduced into the market; but they have not gained widespread acceptance. This is due to measurement and calibration difficulties, low reliability and high cost involved. Some recent analyzers include automatic calibration. The main advantage is that CO level is sensitive to fuel/air ratio variations and infiltration does not have a noticeable effect on the CO reading. They have not been used in spreader-stoker or cell type bark boilers with good success. Rashmikant (1977) described a combustion control system based on CO measurements which is essentially the same as that based on 02 measurements. Since the CO value is sensitive to air/fuel ratio, an additional measurement is necessary (such as 02 or opacity) as a basic requirement for control. Carbon monoxide measurement can then be used to modify the control action in an adaptive fashion for correction. Although there are specific energy savings reported for 0 2 measurement based combustion controls (Keyes and Kaya, 1979), there is not sufficient information on additional energy savings due to the combustion controls based on CO measurements. Taylor (1979); Boehl and Gelineau (1979); Nanney (1976) reported on combustion control of wastewood boilers. Each of the articles is related to a case study of utilizing the steam energy for electric generation or other use.

Energy conservations on auxiliary equipment. Other energy conservation measures dealing with auxiliary equipment have been reported. Myers (1979) reported energy savings on fan applications due to selecting the type of regulating device, and type of driver. Nailen (1978) reports on energy recovery by induction generators. Hester (1979) reports on the conceptual design and economic analysis of a heat pump system for a pulp and paper plant. Gripp (1980) and Mokrytzki (1980) presented the energy savings on adjustable speed drives and described major considerations to be addressed in the application of such drives. Each author used a different method of adjustable drive system; but, both recommended them for high horsepower motors, 2000 and 1000 hp, respectively. Energy conservations in pulp and paper processing.* The application of known concepts such as adaptive control, modern control, large-scale

* The work on the optimization of a pulp and paper process have been done sucr,cssfullyby K. J. A,str6m and co-workersin the 1960s.In this paper, the recent work related to energy will be discussed,

115

modeling and multilevel control have been proposed. Fjeld (1978) introduced linear quadratic optimal control theory to the control of a pressurized headbox and to the control of thick stock consistency. Computers were used to implement the theory. Kalman filtering techniques were utilized. Years of applications experience proved that the systems were successful. But gains were marginal when compared to designs with far simpler structures. Johnson (1978) has analyzed the control of processes making up the wet end of a paper machine, including the kinds of measurements to be taken and how they should be related. He proposed various control sub-loops leading into an integrated control system providing improved process and energy savings. The methods he proposes are classical ones, including feed forward and cross-limiting techniques. However, his approach paves the way for immediate implementation. Betrucci (1979) reported that adaptive gain con. trollers replaced the existing linear (PID) controllers in two applications. The applications were: the control of a blow heat accumulator, and the consistency control for paper machines. Micro, processors were utilized for implementation. Powell (1979) reports significant energy savings by microprocessor-based batch digester control. The paper deals with process controls (low level) as well as high level controls. Gilberth (1980) reports on water conservation and waste heat energy recovery balance to prevent the heating of process water by steam. Coulson (1980) analyzed the four major variables for a dryer section including their relative importance. Then, the author used these four variables to evaluate the economic performance of a dryer as a base for energy efficiency. Finally, Rogers and co-workers (1979) simulated the Kraft mill process and its unit operations for process control and economic analysis. Their approach is systematic, and large-scale system and operations research methods are utilized. Their work on process modeling provides useful background for energy management as well as plant design.

Second-level supervisory controls Supervisory functions at the second level determine the optimum set points of individual units (such as boiler and turbine loads) to assure an overall optimum operation. The major supervisory functions reported are: boiler load allocation; turbine load allocation; soot blowing; cogeneration; and tie-line control. Boiler load allocation. Recently several authors

A. KAYA and M. A. KEYES,IV

116

(Beiter, 1977; Blevins, 1979; Leffler and Shigemura, 1978; Cho, 1978) have reported on optimum boiler load allocation. A simple load allocation problem is defined as follows: minimize C = ~ ci.

(1)

i=1

Subject to the constraints (2)

D = ~ ml i=1

m.mi. ) ~
(3)

ci = f(m,)

(4)

where C is the total cost of steam S/h; c~, cost of steam of each boiler, S/h; D, steam demand, kg/h; m, individual boiler load, kg/h; f, cost function of each boiler; n, number of boilers. It turns out that the necessary condition for optimum loading is

0mx

0m 2

0m3

0m.

(5)

Equation (5) gives the optimum solution when the solution falls within the bounds given by (3). Otherwise the solution is suboptimal and the loads may be at the bounds described by (3). Several methods of solution were reported by Laspe (1978) and included linear programming, mixed integer programming, gradient search, and incremental cost. Cho (1978) converted hard constraints in (3) into soft constraints and solved the unconstrained problem by Nelder-Mead method. Beiter (1977) and Cho (1978) assumed a quadratic function, f, for cost in (4). Leffler (1978) applied the following method to optimize the boiler loads. They increased the load of the boiler with minimum incremental cost, Af/Am, on increasing load demand and conversely. Such an algorithm does not seek the optimum condition given in (5). The algorithm changes the load of one boiler only on a load demand change. However, for a given demand change, the optimum condition can only be reached when all the boilers have the same incremental cost values. Since for a load demand change the values of optimum loads also shift, such an algorithm will not reach the conditions given in (5) in one step. However, their method is useful if the demand on steam changes continually. Especially when load demand swings up and down, the individual unit loads approach their optimum values. It is also possible to approach the optimum loading by increasing the boiler load with minimum incremental

cost and decreasing the boiler load with maximum incremental cost while the demand remains unchanged. Continuing in this way, the incremental costs of boilers will eventually become equal. The problem can be solved through computerimplemented search methods. The static load optimization is treated here. Dynamic cases are handled by the dedicated controls of each unit. This way the highly complex problem is decomposed and each is handled at a different level. Referring to Fig. l, it must be kept in mind that the average black liquor use of recovery boilers is determined by production levels and cannot be altered. The fuel costs of each boiler has to be known for optimum load allocation. That is, the cost figures for purchased fuels and an equivalent sale price for bark should be used. Naturally, a cost value can also be attached to black liquor. The cost values of black liquor and bark are immaterial as long as the costs of alternate fuels to replace them are much more than their assigned cost values. In that case their costs could be assigned as zero to simplify the problem. In summary, there are two methods of implementation of boiler load allocation: (1) load assignment by considering total number of boilers, (2) load assignment by considering individual boilers. For the first method, the problem is formulated by (1)-(4). Equation (5) provides the necessary condition for optimality. However, due to the nonlinearities in cost curves, the solution may be local or global. Also, the operational limits on boilers often prevent the attainment of equation (5). The frequent changes on boiler performance require the updating of the cost curves. The statistical estimation techniques required to obtain a simple cost curve from point measurements for each boiler require computational power. Although this approach is useful for a steady plant operation, it is often cumbersome for the varying load and performance conditions for the boilers in pulp and paper mills. It appears that the second method of load assignment (by the incremental cost of individual units) has wider applications due to its simplicity, speed of implementation, and minimal computational power (hardware and software). The suppliers of energy management computer systems prefer this simple method (Leffler, 1978; Blevins and co-workers, 1980; Cho, 1980). The incremental cost value of each boiler is calculated by the cost and load values for two different loads such as Af f'-f Am m' - m'

S/kg

(6)

where f', f are steam costs S/h; and m', m = loads, kg/h.

117

Energy management technology in industry

f

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df ~"

INC COST CURVE

(_,.).(.,_) .m, A \m-B

f $ df $ --mm STEAMCOST,i~ ; ~ - INC. STEAMCOST(COSTOERIVATIVE), n - EFFICIENCY:f - COST,

FIG. 2 Comparison of steam cost and incremental steam cost values, However, Blevins and co-workers (1980) and Cho (1980) suggested the steam cost, f/m, for each boiler to be used for load assignment. This measure is the simplest way of optimizing the boiler loads and it does not require the knowledge of cost curves. However, using the incremental steam cost is desirable, since steam cost values differ from incremental steam cost values as shown in Fig. 2.

Example on boiler load allocation Consider the plant in Fig. 1 in which B4 and B5 are base-loaded by bark and liquor, respectively. Since the boilers 1-3 are operating with high-cost fuel (gas and oil), their load will be optimized. It is not unusual to have these three boilers operating with bark and oil or gas. The cost curves can still be obtained without any undue difficulty. The optimization problem is illustrated as follows: 3

minC=

~ci=cx+c2+ca.

(1')

i=1

Subject to the constraints 3

D = ~ mi =mx +

m 2 d- m 3 ---- 450

(2')

i=1

100~
i=1,2,3

(1) Nonlinear constrained optimization. Several initial starting loads gave almost the same optimal load values (Kuester and Mize, 1973). (2) Finding the derivative of cost curves df/dm, vs load, and finding the solution by equating df/dm values. The starting loads corresponded the lowest demand. The load was assigned to the boiler with minimum df/dm as the demand was increased step by step. This way the load assignment of each boiler corresponds to equal df/dm values whenever possible. Only #2 and #3 boilers had equal df/dm values. The value of (df/dm) = 2am + b for boiler #1 was considerably lower throughout the load range and # 1 boiler was fully loaded. (3) Starting with equal percent loads for each boiler, then changing the loads of individual boilers by small increments until all (Af/Am)= ( f ' - f ) / ( m ' - m ) values are equal. The load of boiler with minimum Af/Am value was increased while the load of boiler with maximum Af/Am was decreased and so on. There was a great saving on computational time for the algorithm (3).

(3')

cx = --0.0654 x 10-am 2 + 2.453mx + 7.22 c2 = 0.9513 x 10-am 2 + 2.304m2 -- 0.6 ca = 0.8235 x 10-am32 + 2.404m3 -- 13.6.

computational convenience. Three different computational algorithms for total boiler system optimization were used. Each method gave the same results. The algorithms are:

(4')

the load values and demand are in thousands for

Furthermore, the other methods such as suboptimal loading by minimum steam cost method and equal per cent loading were included for comparison. This case study reveals the savings and advantage of optimization by incremental cost method. The comparative results are shown in Table 4.

118

A. KAYAand M. A. KEYES, 1V TABLE 4. COMPARATIVE DATA ON OPTIMUM BOILER LOAD ASSIGNMENT

Optimization by steam cost

Equal °//o loading #1 #2 #3

Boiler load assignments Demand, ton/h Steam Cost, $/h Savings, $/h Savings, % Savings, $/yr

150 150 150

151.5 198.5 I00

450 2 265.66 1.98 0.087 15 840

450 2 267.64 0 0

Optimization by incremental cost 200 144.5 105.5 450 2 247.39 20.6 0.89 162 080

Savings are based on ($4.00 x 106 kJ) fuel cost and 8000 h/yr of operation.

during the transient conditions. Furthermore, the temperature changes create thermal stresses and fatigue which reduces the boiler life. When boiler cost characteristics differ among the boilers, optimal policy will require load shifting. This fact can be demonstrated with a simple two boiler system feeding a common header. The overall and unit cost data of two boilers are as given in Fig. 3. For a given load demand, there are various combinations of loads where (Ofl/~gml) = (df2/t~m2)

Note that the optimization by individual boiler steam cost can be disadvantageous as compared with the equal percent loading. This matter will be discussed in the following section. C o m m e n t s on optimization. There are certain conditions to be observed in determining boiler load allocation policy. The most important one is that the boiler loads should not be shifted often or drastically. Boiler combustion efficiencies are poor f / ~ fl = --.5m12 + 10ml

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Energy management technology in industry

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for optimum conditions as described in Fig. 3(b) and (c). It turns out that the optimum allocation requires load shifting as can be seen from Fig. 4. It is possible to find suboptimal policies which eliminate the load shifting as seen in Fig. 5. Also, overall cost of steam versus demand for various optimal and suboptimal policies are given in Fig. 6. There is additional future work to be done on determining optimization

of optimal policiesfor ease 3. methods for particular applications to provide practical and safe operation (satisfying vs optimizing). Dziubakowski and Keyes (1980) discuss the safety, security and maintainability of the power house along with optimization. Next, referring to Fig. 3 the analysis of equal percent loading versus minimum steam cost (steam cost = f/m) loading will be discussed. Consider the

120

A. KAVA and M. A. KEYES, IV

steam demand equal to 15. The equal percent loads will be at points A and B of Fig. 3(a) and the steam cost will be at point C in Fig. 3(c). Now consider a demand increase of one, and then a demand decrease of 1. According to equal percent policy, unit loads will become 8 each and then 7.5 each. Finally, the loads will settle at A, B, and the cost at C. However, per the algorithm of steam cost optimization the unit #2 will become 8.5 and then unit #1 will become 6.5 (points A1, B1). This corresponds to a cost Ca which is higher than that for C of equal percent loading. Incremental cost method will minimize the cost of steam for the same conditions. Note that unit g 1 will take the increased demand and the unit #2 load will be reduced on demand decrease. This will settle to the load points of A2 and B 2 and to the cost point of C 2 which is lowest among the others.

the same pressure header. The steam flows should be calculated in terms of $/h rather than J/h. Inflows should be assigned a value equal to the cost of steam, from boilers. The extraction flows should be assigned a $ value--equal to their utility values (thus recognizing second law consequences of differing supplies). The generating turbines are primarily used for low-cost electric generation while the steam demand of the plant is satisfied. The general turbine optimization problem is then to minimize the cost of generated electricity while the steam demand is satisfied. This problem can be simplified by decomposing it into two subproblems, as below. The particular subproblem to be treated is the result of a higher level decision as 'buy or generate' (a) electric demand-based optimization: for a given demand of in-plant electric generation, the utility value of energy used due to the electric generation is minimized while the electric demand is satisfied; (b) steam demand-based optimization: for a given steam demand of each pressure head, the electric generation of the plant is maximized while the steam demand is satisfied.

Turbine load allocation Turbine load allocation is very similar to the boiler load allocation problem, particularly when the turbine system to be optimized is fed from a common pressure header and all turbines are condensing turbines, or exhausting to a common pressure head. There are slight complications in real turbine systems since the energy from intermediate stages of extracting turbines can be utilized in several ways. The turbines may not all be fed from

In problem (1), condensing flows may be necessary to satisfy the electric demand, while in problem (2) this is not a necessity. 6635kPe,3312kJ/kg

r'-

.

.

.

.

f I

x~s

_(ix '

WATE,___._ --

.1 c x22 WATE~

X12

3.3

, ~7

lx"

Xll

X23 X16

X19

X5

X13

ix1(

X24

rX8

L

X11

X25 209kPa,r/44 k,J/~

NOTE SAT.WATER@ 121C, h m 609kJ/ko FIG. 7. Schematic of turbine system.

CONOENSATE

121

Energy management technology in industry 160:

I

I

MAX FLOWTO THROTTLE

12o

THROTTLEFLOW IN 1,000-kg/h

Tx,g -"

00

00

/

/ 40

20

0 0

4,000

8,000

! 2,000

! 4,000

12,000

10,000

TURBINE NO. 2

20,009

GENERATOROUTPUT- KW ! 6,g00

| O,OOO

! 10,000

:

-~

TURBINE NO. 1

FIG. 8. Performancecharacteristicsfor turbines #1 and #2.

Referring to Fig. 1, a portion of the plant is identified as a turbine system. The turbine system is bounded by broken lines as shown in Fig. 7. Again, as in the boiler system, a total turbine system optimization or individual turbine optimization can be pursued. A total turbine system optimization problem can be treated as a linear programming problem. Hanson (1978); Hunt (1980); Aarnia (1980) and other workers identified the turbine optimization and reported the results of their work. In the following, a specific example is treated. Example on turbine optimization Consider the turbine system as in Fig. 7. A turbine system optimization is based on steam demands x 12, x la, x 14 specified on the steam pressure headers. Here the generated electricity is maximized while the steam demand is satisfied. This is equivalent to minimizing the cost of generated electricity. A penalty is paid for condensing flows as reflected in an objective function. Objective function maximize:

x 2 6 -~- x 2 7 -~- x 2 8 - a1~Xxx

(7)

where x26 , x27 , x28 = power/generation, MW; Xll = condensate flow; a l i = penalty coefficient for loss due to condensate flow. The value of a11 is

The energy flow into condensate is assumed to be a loss. Also, an additional penalty is imposed as CTC = 0.5 which is related to the energy required for the condenser to condensate the steam. The value of all can be modified per operating conditions. The constraint relations to be satisfied are below. Referring to Figs 8 and 9 (Hanson, 1978), the turbine performance relations can be written as follows. The details of derivation of equation (9) are given by Hanson (1978). Generation-flow relations turbine 1:2x26 = 0.222xs + O.110x7 + 2.78 turbine 2 : x 2 7 = 0.222xtt + O.110xlo + 2.78 turbine 3: x2a = 0.025x 3 + 0.250x5 + 0.125x4 + 1.75. (9) Maximum throttle flows (in respective order) x6 ~< 148; x9 ~< 148; x2 ~<285.

(lO)

Limits of exhaust flows 6 <~xs <~80; 6 <<.xl~ <~80; 5 ~
(11)

Maximum extraction limits for turbine #1 2442 a l l = (1.0 + CTC)3412.

(8)

x4 ~< 137; x3 ~< 180.

(12)

122

A. KAYA a n d M . A. KEYES, I V

MA,X.THRO'rI'LEFLOW i "/ / /

300 260 260

n6

/,~

260

,

/<

r//

/

g]/

.

,,o

THROTTLE FLOWIN 1000 kg/h

/,

,66

,,o --'Z

160

d,Yl

, ~ ,e,. '

1

411 2G 0

6,000

8,000

10,000

12,000

14,000

16,000

18,000

2O,OOO

GENERATOROUTPUTKW FIG. 9. Performance characteristics for turbine #3.

TABLE 5. SOLUTION OF TURBINE OPTIMIZATION PROBLEM

Flows at headings, 1000 kg/b

Turbine

3143

1047

209

Cond.

Generation (MW)

0 -60

80 126 34

-6 --

7.48 12.22 18.74

generators

#1 #2 #3

---274

- 80 - 132 180

Pressure reducing

6635-3143 3143-1047 1047-209

- 158 0 --

172 0 --

--0

0

----

--

140

-60

-240

-6

To users

MW

6635

generation

---

-38

PRV energy balances

limits of turbines

3.5 ~< x26 ~< 9.4; 7.0 ~< x27 ~< 18.8; 7.50 ~< x2a ~< 18.74.

3 3 1 2 x 1 5 + 509X2o - 3 0 9 3 x 2 1 = 0 (13)

3093x17 + 509X22 -- 2954x23 = 0 2 9 5 4 x 1 9 + 5 0 9 x 2 4 - - 2 7 4 4 x 2 5 = 0.

Turbine

(16)

mass balances Mass balance of pressure heads X2 -- X3 -- X4--

X5 = 0

X21 "1- X 3 - X I 6 - X X 6 -- X 7 -- X 8 ~

X9-XIO-Xll

(14)

= 0.

X23 - ~ X 4 - - X

x25 + x5 + x s Steam demands

PRV mass balances X 1 --Xl5

6-X

9-X12

~ 0

7 --X18--X13

~ 0

0

= 0; X21 - - X 2 0 - - X 1 5

= O.

(17)

at pressure heads xx2 = 140

~-~ 0

x~3 = 6 0

X16 - - X17 ~-~ 0; X23 - - X22 - - X17 = 0 X18 - - X19 ~-~ 0; X25 - - X24 - - X19 ~-~ 0.

+ Xlo-X14

(15)

x14 = 240.

(18)

Energy management technology in industry 6635 kPa

1047 kPa

3143 kPa

\

/

PRV \

/

PRV

FIG.10.Alternativeflowpaths to satisfyincreasedsteamdemand in 1047kPa line. The solution of the optimization problem is given in Table 5 in a matrix form. This result can be used as a basis for the mass and energy balances of plant operation. As the steam demand of the plant changes the optimum values will vary throughout as dictated by the results of the linear programming problem.

Optimization by sinole units However, the optimization by the single turbine units can be provided without solving the total optimization problem repeatedly (by linear programming method). Consider the optimum operating conditions as obtained by the previous solution. Next, consider an increase Ax~3 in steam demand in 1047 kPa line. This demand is satisfied by the following alternative flow lines as shown in Fig. 10. Since PRV flows should be avoided, the alternate flow paths from Fig. 10 are A: x2 ~ xa--}x6 ~ x 7 (turbine #1 then turbine//3) B: x 2 ~ x 4 (turbine #3). (19) Considering the maximized power generation concept a selection should be made between x#, and, x3 and x7. Examining (9) reveals that the power generated in each case is A: AMW = 0.025Ax 3 + 0.110Ax? Ax3 = Ax7 = Axl3 AMW = (0.025 + 0.110)Ax13 = 0.135Ax13

(20) B: AMW = 0.125Ax# Ax,, = AXz3 AMW = 0.125Ax4.

(21)

Clearly the choice A generates more power and should be preferred. However, x3 cannot be increased further since x 3 = 180 = x3~m,). The only choice is B. It is understandable why x3 is at its maximum for optimization.

123

Optimization by single units can be an effective way to respond demand changes, once the solution of linear programming problem (shown in Table 5) is provided. Very limited literature is available which describe the systematic methods in turbine optimization. The performance curves given in Figs 8 and 9 should be updated by on-line measurements. The pressures at headers vary which in turn effect the turbine performance curves and should be included. Aarnio (1980) identified these effects. Again, the static optimization is treated. The dynamic control and optimization is handled by lower level dedicated controls. Individual turbine controls can perform to minimize a given performance index (error criteria).

Soot blowing optimization Judicious soot blowing minimizes the cost of steam. Excessive soot blowing increases heat transfer and steam generation for a given fuel input. However, after the steam used for soot blowing is subtracted, the net results may be lower. That is, an optimum soot blowing policy should be defined, measured, and enforced. Soot blowing should be applied to the sections of boilers where it is necessary, rather than a simple time programmed cyclic regimen. Also, the black liquor recovery boilers in the Kraft pulp process represents the most lucrative potential application of soot blowing controls. A mathematical approach to this subject was given as early as 1965 by Chappel and Locke. Programmed microprocessors are being used today for judicious soot blowing as reported by Hayman and Pelletier (1976). In 1978, a limited optimization method was tried on a recovery boiler as reported by Bishop (1979). The future trends will include the minimization of steam cost due to soot blowing; better measures of boiler state to insure protection of the boiler; and, better detection of incipient fouling and slagging problems. The present approaches on soot blowing optimization are based on trial and error methods. Bishop (1979); Hoynalanmaa (1981); Laszlo (1981), used predetermined values for fouling and slagging indicators in a given section to trigger the soot blowing equipment. Some of the indicators considered for each boiler section are as follows: pressure drop on gas side; temperature drop on gas side; temperature drop on water side; overall heat transfer coefficient (dirtiness); and time elapsed since last soot blowing. The values of the indicators are first assigned arbitrarily; and later based on the past experience. It takes some time (about one year) to arrive at some reasonable setting for some particular application. The measurements are needed for soot accumula-

124

A. KAYA and M. A. KEYES, IV

tion with respect of time and required energy to clean it. Chambers, Wynnyckyj and Rhodes (1980); Heberer and Wison (1981); Heil, Nethercutt and Scavuzzo (1981) reported the measurement techniques for boiler cleanliness. They reported the results of continuous measurements including the effects of soot blowing. There is no reason why these measurements cannot be used as a basis to develop an optimization problem for determining an optimum soot blowing policy. Kaya (1981) considered a soot blowing optimization problem for one section of a boiler, based on the measurements mentioned in the preceding paragraph. The author calculates: (a) cost of steam, (b) dirtiness of surfaces, (c) required energy for cleaning, each with respect to time. Considering the time interval between cleanings as a variable, an optimum soot blowing policy was obtained for a given time period (e.g. one day). The function to be minimized was the total cost of operation with respect to cleaning interval, AT, for a given time period T. The cost function I is AT

/(AT)

2AT

= f S(t)dt + f S(t)dt +... 0

AT nat

f S(t)dt +nC(AT)

+

iAT

(i - 1 )AT

(T/AT)

~C

i=1

AT

E [½:(AT)2] +

T 1

=

z

C T

S r E: (AT) ] + AT

= T~(AT) + ~-TT.

(24)

Setting (d/MAT) = 0 gives the optimum result as

dI(AT) T CT d(A---f)-=0=ya-(AT)-----

(25)

A T = 2X/~-.

(26)

For the values of C = 5, $; ~ = 0.3, $/h2; T = 24, h. The result AT = 5.77 h is found. Of course, the functions S and C are simplified to demonstrate the technique. Figure 11 is a graphical representation of functions. The future work in soot blowing lies on dynamic measurements and modeling to answer the questions: (a) when to blow? (b) where to blow? (c) how much to blow?

(n - 1)At

iAT

nC(AT) (ik

_

_

(22)

I)AT )

"V

~.

cost of dirtiness

V

)

cost of blowing

where AT is the time intervals between cleaning, h; T, total time period, h; n = (T/AT),number ofcleanings within the time period; S, cost of operation due to the dirtiness, S/h; and C = cost of each cleaning, $. The minimization of I with respect to AT provides optimum soot blowing interval, as dI(AT) d(AT)

-

=

0.

(23)

Example on soot blowingoptimization.Considering A -- C, $ (constant); S = at, S/h, (a-constant); and T - - 2 4 , h. The optimization function to be minimized becomes iAT

(i -- 1 )AT

There is not sufficient data base for an 'on-line' soot blowing optimization. The other means of soot blowing such as the compressed air and sonic horns have been considered. The sonic horns are used to maintain the cleanliness rather than the cleaning. The idea is to keep the particles from sticking to the surfaces by vibrating the section via sonic horns. Lange and Schwartz (1980) reported the results of soot blowing by sonic horns in a paper mill. They provided the energy savings over the conventional soot blowing equipment whenever sonic cleaning is possible. However, sonic blowers can not solely be depended upon for soot blowing and must be used in conjunction with the conventional soot blowers.

Cogeneration Cogeneration is a supervisory function over the use of extracting turbines to produce electricity while providing low pressure steam for other plant uses. The energy management problem in cogeneration is to cost optimize the relative production of electricity and steam. The scope of the cogeneration

Energy management technology in industry

125

(a) COST*TIME RELATION ~T , - ~

L

,~

~T

T

~..._.

COSTOF DIRTINESS

$/H

.

TOTAL

(b) COSTVS. SOOT BLOWING RELATION

] 2

I 4

I

I 6

I 8

n - (T/~.T)

FIG. 11. Graphic representation of optimum soot blowing.

problem includes the determination of the following aspects (1) The demand of electric power and steam generation. (2) The means of powering (electric or steam) the mechanical drives (fans, pumps, compressors, etc.) in the plant. (3) The cost of electricity by in-plant generation compared to the cost of purchased electricity to establish a basis for purchased power vs in-plant generation options. (4) The amount of purchased electricity, such that the cost of energy per unit product is minimum and the fuel constraints are met. Optimization of cogeneration is based upon lower-level optimization. These are primarily the turbine load allocation and boiler load allocation functions. The plant demand for electricity and steam is provided by the plant energy management function based on production requirements. The cogeneration optimization, in turn, determines the source of allocation and the cost of steam and electricity. Depending on the cost parameters, optimum cogeneration can result in selling electricity during peak demand hours to reduce the overall cost of the unit product.

P e a k Demand T a r g e t

KW-h

End of C o n t r a c t Period

Time

Fro. 12. Tie-line control band. Tie-line control If the rate schedule is complicated, the cogeneration optimization and tie-line control become complicated also. One version of tie-line control is given in Fig. 12. The purchased power is to remain within this band. The band narrows and converges to the value of the peak demand target at the end of the contract period. The shape of this band also should be determined at a higher level in the hierarchy depending upon future production rate. Another useful purpose of this band is to minimize the control actions early in the demand period. This control may also be named 'demand-limit control'. A data acquisition system monitors the electrical loads and supplies in the plant. If the electrical loads reach the limits which can be provided by purchased

126

A. KAVA and M. A. KEYES,IV

power, the loads will shed per a predetermined schedule. If an electric load increase is required, optimum cogeneration will determine the policy to be followed--generate or buy. The elements of tie-line controls are listed below:

gathering aad monitoring, off-line calculations, and energy status reporting. They presented some results on boiler and turbine optimization including tie-line control. They recognized the need for higher-level energy management above the unit optimization. They exercised several ad hoc type measures to improve the performance of energy management system. Kaya (1978) presented a four-level energy management system in the order of increasing level of complexity. They are: control, supervisory, coordination (plant energy management), and planning (factory management). A list of information communicated between various levels is given for a typical pulp and paper power house. In Fig. 13, a structure for an energy management system is given (Kaya, 1978), including the required information flows in brief form. Rogers (1977, 1979) studied the pulp and paper process economics and energy use. His approach is systematic and utilizes large-scale optimization methods. Recently Reside and co-workers (1981) reported on the energy management of a Kraft mill. The work covered the statistical data on energy use of various units in Canadian and Scandinavian paper mills. They recommended some energy conservation measures as well. Their work is useful as a database for overall plant energy management.

(!) Method of electrical demand control error. (2) Utility power monitoring. This monitoring system has features such as: (a) high speed load shedding; (b) VAR management; (c) automatic synchronization of generators and ties; (d) real and reactive monitoring. Several workers (Leffler, 1978; Wilson, 1979; Ross, 1979) have reported on the subject of cogeneration and tie-line control. A systematic treatment of cogeneration as a higher-level optimization problem should be emphasized in future work. Plant energy management Several workers have discussed the overall energy management of an industrial plant (Aarnio, Tarvainen and Tinnis, 1980; Blevins, 1979; Kociuba and Ponstingl, 1977; Tsai, 1977; Swanson, 1977; Blevins and co-workers, 1980; Cho, 1980; Scott and Bradford, 1980). Their approach to plant-wide energy management is at the stage of extensive data Factory

I

M enagement

i

Energy Management

System

~ PiuntEn.rgy I

Energy Production

..--g.m.n,

Management

~.~

~

I

I

Energy

1

[

I

[..n..m.nt

I

I ".nag'm'n.

J

I Transmissi°n i

Energy

J Consumption

Other Energy (Solar r Wind~ etc.) Purchased Energy J By Product Energy J "l I

Boilers Turbines, Generators.

Lines

PIpes Process

Collectors

(Energy Production

Plant)

A. Commands B. Measurements 1. Purchased Energy Environment Estimates Production

2. Energy Cost/Product In-Plant Energy

Readiness 3. Energy Demand Energy Availability Enviormentel Constraints

(Energy Consumption

Plant)

4. Energy Produced Efficiency Resources Utilized 5. Energy Inventory Scheduled Maintenance E. Energy Delivered, Received Efficiency Energy Recovered 7. Energy Availability, Production Demand 8. Energy Consumption/Production Efficiency By-Product Energy

FIG. 13. Structure of energy management system for industrial plant.

Energy management technology in industry

Load scheduling underfuel availability constraints. Fuel having a high kJ/$ ratio is scarce. The load and fuel selection schedule of boilers must be constructed to prevent fuel unavailability. Boiler and fuel utilization limits can be set by higher-level heuristics to provide continuous operation.

Inventory, monitoring, alarming. The industry has to meet a number of regulatory and operational data gathering requirements such as monitoring of pollution levels, efficiency limits, and energy inventories. Because of this, the literature on the energy management work includes various ancilliary functions: unit and overall performance evaluation and monitoring, trend analysis, logging and alarming, energy inventory summary, alternate fuel allocation, plant simulation, maintenance tracking, report generation, emergency operator guides, sequence of events monitoring, and economic analysis. A successful energy management requires the support of administration as well as engineering know-how. Methven (1979) lists the ingredients of an effective energy management program. Aarnio, Tarvainen and Tinnis (1980) lists the steps of an engineering approach to energy management. Future work is necessary to develop a systematic plant-wide energy management system. IMPLEMENTATION

Implementation ranges from a combustion control within an individual boiler to a total energy management system for an entire plant. The energy management aspects of implementation will be discussed here.

Combustion control system Controlling the fuel/air ratio by the measurement of ~oO2 in stack gases provides accurate control and significant savings, especially in multifuel stations (e.g. Gunsaulus and Johnson, 1978). The savings range from 2-10~o in the majority of the boilers depending upon the existing equipment. For a typical boiler with 1.5 x 10 6 kJ/h fuel consumption, a 3~o efficiency improvement provides $160000/yr saving based on $4.45 million kJ fuel cost and for 8000 h/yr operation. The cost of installing the control and instrumentation ranges from $15000 to $75 000, depending upon control sophistication. Combustion controls by the measurements of CO have also been introduced as a part of a computer system. CO sensors are about ten times as expensive as 02 sensors. A study on the return of incremental cost of CO sensors over 02 sensors is desired. Although there are advantages of CO based combustion controls, the best results can only be obtained when additional sensors such as 02 or

127

opacity are jointly used. The control diagrams of combustion controls are given in the literature (see the section: Description of methods/combustion controls). Analog controls and measurements are widely used for this low-level control system. However, there is an increasing trend to shift to microprocessor-based control units. Measurements are so far unchanged. Control functions of digital devices are mostly the same as their analog counterpart (PID, etc.). One other advantage of digital controls is their ability to communicate with other computers via a databus. Combustion control is the key to combustion efficiency. Even a combustion control contains several minor control loops for optimum combustion to maximize boiler efficiency.

Energy management and optimizing system There are numerous workers who have reported their field experience in applying computer systems to energy management. Their work is introduced in the section: Description of methods. The equipment offered is a microprocessor-based distributed computer system. The cost of a system ranges from $300 000 to $1000 000. The functions performed are usually higher-level supervisory energy monitoring, and others such as reporting, alarming, etc. As reported by Keyes (1976) the conventional digital process control computers are at their maturity. The trend is to introduce more flexible, lower cost distributed computer systems for energy management. Such systems can be retrofitted into the existing controls and instruments. Some installments have low-level digital controls with analog back-up. Analog~ligital interfacing is required to implement these systems. Scott and Bradford (1980) described a threephase approach to a paper mill energy management as a case study. Blevins and co-workers (1980) described an energy management package for industrial plants. A representative multilevel energy management system is given in Fig. 14. The system has the capability of data monitoring, display, and reporting. A distributed computer system is the key to energy management. Implementation of energy management starts with low-level control and instrumentation. An industrial boiler has about 30 transmitters and 6 control loops. However, only boiler load vs overall efficiency of each boiler is needed for optimum boiler load allocation. For cogeneration, the steam cost versus steam demand of the powerhouse is only needed to determine the cost of generated electricity. The turbine system and other process units should be treated similarly. CONCLUSIONS

In this paper, the philosophy and concepts of

128

A. KAYA and M. A. KEYES, IV Factory

Management Plant Energy Management | COMP,PRT,CRT,KB,I OTCO ] I Energy Production J Management COMP, CRT, PRT,KB, OTCO

I

BOILER CONTROL ROOM RC,10, CRT,OTCO

DATA BUS

¢ I REMOTE

I

TURBINE CONTROL| ROOM ] RC I/O,CRT,OTCO J

Commands of load Settings (operator bias) Remote Interface

/ \

/

Measure- Commands ments of Load (steam Settings flow) (Turbine valve setting) C CRT PRT RC

power) \

/ Measurements (elec. power gen.)

: Dedicated Control : Cathode Ray Tube : Printer : Remote Control

RC,I/O

Meas. (Mech.

valve setting)

,~

I

REMOTE INTERFACE UNIT

INTERFACE UNIT RCII/O

of (Turbine

¢

I

MEASUREMENTS

-I

Commands (Prod. rate)

~

/ Meas.

(product energy

status)

KB : Keyboard I/O : Input-Output Devices OTCO : Other Communications COMP : Computer

FIG. 14. Hardward schematic of energy management system.

energy management have been discussed along with a survey of the technology. As a further contribution, the examples on boiler and turbine system optimization at various levels of sophistication are worked out. At this time, various energy management functions and energy conservation hints are known in industry as given in Appendix A and B, respectively. The current work has concentrated on optimization and higher level controls. Total plant energy management is not at its complete maturity. The future challenge will be on using largescale system methods for energy management. The methods of partitioning, decomposition, and system modeling will be more utilized due to the distributed nature of microprocessors. A decentralized control system should be developed in which communications with off-line large computers will be utilized. REFERENCES Aarnio, S. E., H. J. Tarvainen and V. Tinnis (1980). An industrial energy management system. TAPPI, 63, 73. Balchen, J. G. (1979). The applicability of modem control theory related to dynamic optimization in industry today. Control Engineering, On-line Optimization Techniques in Industrial Control, (edited by E. J. Kompass and T. J. Williams). pp. 51-62.

Barry, W. J. and co-workers (1976). Computer-based operator guidance system for energy management. TAPPI, 60, 107. Beiter, E. (1977). On-line energy management--a real time information and control systems. ISA Conference, pp. 187-189. Bertucci, J. A. (1979). Application of adaptive gain controllers for energy savings in the pulp and paper mill. TAPPI Engineerin# Conference, pp. 285-298. Bishop, D. R. (1979), Recovery boiler computer control ... development of soot blowing optimization. Southeastern TAPPI Conference, pp. 1-12. Blevins, T. L. (1979). Applying energy management in pulp and paper mills. TAPPI Engineering Conference, pp. 263-277. Blevins, T., D. Roberts, L. Block and C. Andreasen (1980). A standard software package for energy monitoring and energy steam optimization in the pulp and paper industry. ISA/80 International Conf. and Exhibit. Paper C.I. 80-562. Boehl, H. E. and E. D. Gelineau (1979). Control systems design. INTECH, 26, 35. Chambers, A. K., J. R. Wynnyckyi and E. Rhodes (1980). A furnace wall ash monitoring system for coal-fired boilers. ASME Paper 80-WA/Fu-I. Chappel, R. E. and J. W. Locke (1965). A mathematical approach to automation of soot blower controls. American Power Conference, Chicago, IL. Cho, C. H. (I 978). Optimum boiler load allocations, instrumentation in P & P industry, Vol. 17, April 1978. Instrument Society of America, pp. 39--44. Cho, C. H. (1980). Computer system applications in energy management. IFAC Workshop, Systems Engineering Applications to Industrial Energy Generation and Processes. Coulson, L. L. (1980). Optimizating an existing dry section. TAPPI Engineerino Conference, pp. 33-43. Dziubakowski, E. J. and M. A. Keyes (1980). Control system

Energy management technology in industry design for effective energy utilization, a case study. 2nd Annual Conference on Industrial Const. Tech., Houston, Texas. Fadum, O. C. (1980). Application of control to improve energy efficiency in pulp and paper, IFAC Workshop, Systems Engineering Applications to Industrial Energy Generation and Processes, Houston, Texas. Fjeld, M. (1978). Applications of modern control concepts on a Kraft paper machine. Automatica 14, 107. Gilbreath, K. R. (1980). A balance of water conservation and energy recovery. TAPPI Engineering Conference, pp. 367-372. Gripp, L. P. (1980). Application considerations for varichron drive systems. TAPPI Engineering Conference, pp. 105-117. Gunsaulus, R. K. and R. K. Johnson (1977). Combustion controls for pulp and paper multifuel boilers. 25th ISA Pacific N.W. Conference, Tacoma, Washington. Gunsaulus, R. K. and R. K. Johnson (1978). Modern control systems improve efficiency of multifuel boilers. Pulp and Paper

52,

64.

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Production schedule to energy availability. Calculations for BTU accounting. On-line energy balance calculations. Monitor steam traps, control condensate recovery. Control PRV steam flow. Measure and calculate heat exchanger performance. Determine the effectiveness of waste product utilizations, recovery. 8. Electrical power factor (maximize).

130 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

A. KAYA a n d M. A. KEYES, I V

Generate noncondensing power (maximize). Building energy balance. Recycle air and water. Alarms for target deviations. Hourly, daily, shift averages. Energy accounting reporting. Energy trend and performance trend analysis. Upset documentation. Blowdown control. Scheduling, inventory control, production facility loading, plant energy model. Coordination of material flow, unit operation and dynamic scheduling. Voltage control and VAR management. Real and reactive monitoring. Optimization of process energy use for maximum overall profit. Optimization of heat exchanger, operation, reflux ratio, insulation thickness.

APPENDIX B. ENERGY SAVING HINTS The following list gives an idea of how energy management and optimization can partially be implemented to produce energy savings. This list is not complete but is meant to be illustrative.

1. Variable pressure operation (use variable pressure to improve efficiency in boilers). 2. Variable fan speed (FD, ID). 3. Open valves fully (reduce heat conversion of work). 4. Series heat exchanger instead of parallel reduces pump power required. 5. Reduce power required of compressor by cooling after each stage instead of cooling once. 6. Obtain 35~ of refrigeration capacity by thermocycle cooling during winter. 7. Burn plant wastes. 8. Install blow-down heat exchanger. 9. Replace steam jets with vacuum pumps. 10. Drive turbines with steam undergoing pressure reduction. 11. Cogeneration by back pressure turbine or gas turbine and waste heat boiler which needs clean fuel (gas). 12. Add distillation column to steam plant. 13. Development of instructions to operators such as: add Freon to machine; remove air from steam condenser, etc. 14. Relocate flow controller. 15. Change refrigerant fluid. 16. Add product analyzer feedback control. 17. Avoid overhead condenser floodings. 18. Reduce cooling water and compressor vapor by-pass.