ENGINEERING PAPER

Optimal Scheduling of Belt Conveyor Systems for Energy Efficiency - With Application in a Coal-fired Power Plant

        Abstract: The energy efficiency of belt conveyor systems can be improved at equipment or operational levels. In literature, variable speed control is proposed as an effective way to improve operation efficiency of belt conveyors. However, the current strategies most focus on lower control loops without considerations at the system level. In this paper, an optimal scheduling is proposed to improve the energy efficiency of belt conveyor systems at the operational level, where time-of use (TOU) tariff, ramp rate of belt speed and other system constraints are considered. A coal conveying system in a coal fired power plant is taken as a case study, where great savings of both energy cost and energy consumption through the optimal scheduling are achieved.

Index Terms—Belt conveyor system, energy efficiency, Optimal scheduling, Time-of-use tariff

I INTRODUCTION

          Belt conveyors are being employed to form the most important parts of material handling systems because of their high efficiency of transportation. Energy cost forms a large

part of the operational cost (up to 40% [1]) of belt conveyor systems. It is significant to reduce the energy consumption or the energy cost of belt conveyors by improving energy

efficiency. A belt conveyor is a typical energy conversion system from electrical energy to mechanical energy. The improvement of energy efficiency can easily put to the operation

efficiency and equipment efficiency. It is also noted that equipment efficiency, and consequently operation efficiency, decides performance efficiency, which is usually reflected by various external indicators, such as energy consumption or energy cost. On the other hand, a performance indicator can drive an operation in its optimal efficiency.

            In practice, the improvement of equipment efficiency of belt conveyors is achieved mainly by equipment retrofitting or replacement. The idler, belt and drive system are the

main targets [2],[3],[4],[5]. In general, extra investment is needed for the efficiency improvement at equipment level; and the opportunities are limited to certain equipment. Operation efficiency of an energy system can be generally improved through the coordination of two or more internal sub-systems, or through the coordination of the system components and time, or through the coordination of the system and human operators [6]. In [7],[8] and [9], the operation efficiency in terms of operational cost of belt conveyors is improved by load shifting, which coordinates the operating status and time. In literature, speed control

is recommended for energy efficiency improvement of belt conveyor systems, even though it is occasionally challenged, e.g., in [10]. The core of speed control is to coordinate the belt speed and the feed rate to keep a constantly high amount of material along the whole belt, which is believed to have high operation efficiency. Nowadays, the idea of speed control has been adopted by industry and successfully applied to some practical projects [11],[12],[13],[14],[15]. However, The current strategies of speed control employ

lower control loop to improve the operation efficiency of an individual belt conveyor [13],[16]. It cannot be used to deal with the system constraints and external constraints, such as time-of-use (TOU) tariff and storage capacities; and it cannot be used to coordinate multiple belt conveyors of a conveying system.

            The main purpose of this paper is to introduce optimal scheduling to belt conveyor systems to improve the energy efficiency. It specifically focuses on the improvement of

operation efficiency by variable speed control. We start with the energy calculation model of belt conveyors. Then the optimal scheduling problem of operation efficiency of belt

conveyor systems is formulated. It takes the TOU tariff into account and considers other relevant constraints to achieve the minimization of a performance indicator employed to

balance the energy cost and a technical specification. We use a coal conveying system in a coal-fired power plant as a case study. The optimal scheduling and the current control strategy will be applied to this coal conveying system, respectively.The layout of the paper is as follows: In section II, the energy calculation model of belt conveyors is reviewed. In section III, the optimal scheduling problem is formulated. The last section is the conclusion. 

II)  An analytic energy calculation model is proposed as follows.
(V,T)=Q1T^2+Q2V+Q3T^2/V+Q4T+V^2T/3.6                (1) where P(V,T) is the power of the belt conveyor (kW), V is the belt speed in m/sec T is the feed rate t/h, and Q1-Q2 are the coefficients which come from the design parameter or are identified by parameter identification. Further V and T also obey the following relation 
T = 3.6 QGV,       (2)
 where QG is the unit mass of material along the belt kg/m. The maximum value of QG is determined by the characteristics of the and bulk material the bulk material [17],[18]. Incorporating with the efficiency of the drive system, model (1) is rewritten as follows 
(V,T)=1/n(Q1T^2+Q2V+Q3T^2/V+Q4T+V^2T/3.6)             (3)
where n is the efficiency of the entire drive system. Further n=nd*nm, where nm is the efficiency of the motor and nd is the efficiency of the drive. In next section, energy model (3) will be integrated into the optimal scheduling problem to improve the operating efficiency of a belt conveyor system in a coal-fired power plant.

III) OPTIMAL SCHEDULING OF A BELT CONVEYOR SYSTEM IN A COAL-FIRED POWER PLANT

To consider optimal operation efficiency of belt conveyor systems, we introduce optimal scheduling with the objective to minimize energy cost in this section. A coal conveying system in a coal-fired power plant at an anonymous location, as shown in Fig.1 is taken as a case study to show the operation efficiency improvement through optimization. The plant has two 600 MW units, presently; and two 1000 MW units will be set up in the future. The coal conveying system is designed for all the four units.

 A. Overview of the coal conveying system The raw coal is delivered to the power plant by a vessel. Two ship unloads along with three belt conveyors, C1, C2 and C3, transfer the raw coal from the vessel to the coal storage yard. Then, the coal is fed to the coal bins of the two boilers through belt conveyors C4-C8 to meet the demand of the two units. Actually, each belt conveyor has its backup standby; and two belt conveyors make one pair. Under the conventional operational mode, only one belt conveyor of each pair runs and another one is on standby. Hence, it is reasonable to take one belt conveyor of each pair for investigation. There is a coal crusher between C6 and C7. Each boiler is equipped 6 coal grinding mills; and

each mill has its own coal bin. In view of system analysis, we are reasonable to treat the 12 bins as an unity. According to the specifications of the plant, the total capacity of the 12 bins,  denoted by TCB, is sufficient to sustain the two units for 11.8 h under rated loads. The feeding process, from coal storage yard to the coal 

 Fig. 1. Flowchart of the coal conveying system

Bins, is suitable for energy optimization because it can be isolated to be controlled independently and has rather large buffer (coal bins) for optimal scheduling. The coal crusherwill not be included in the following investigation because it follows its own control strategy.

B. Current control strategy Coal bins are equipped with ultrasonic level detectors. Further, a sequential control system (SCS), implemented by programmable controller (PC), is employed for this coal conveying system. The SCS calculates the amount of the remaining coal periodically using the readings of the ultrasonic level detectors. For the sake of the feasibility and reliability of the feeding process, an upper limit (HL) and a lower limit (LL) are employed for the coal in the bins, respectively. If the amount of the remaining coal in the bins, denoted by RCB, goes down less than LL, the SCS runs C4-C8 belt conveyors to feed coal to the bins. On the other hand, if RCB goes up greater than HL, the SCS stops the feeding process. The current control strategy takes the on/off status of the feeding process instead of the belt speed as control variable without consideration of system constraints.

C. Optimal scheduling strategy

The current control strategy focuses mainly on the feasibility and reliability without optimization of operation efficiency. Subsequently, we intend to introduce optimal scheduling to the feeding process for optimal operation efficiency. The following assumptions are firstly made in order to model the feeding process as a simplified optimal

control problem.

1) At any time, the coal storage yard always has enough coal to supply the feeding process.

2) The time delay associated with the coal from the coal storage yard to coal bins is ignored.

3) The dynamic energy consumption associated with start-up and stop of the belt conveyors is not taken into account.

4) The coal crusher is not taken into account.

5) At the beginning and the end of each scheduling interval, denoted by ICB, takes a constant and fixed value, which is necessary for the repeatedly implementation of the optimal scheduling.

For the feeding process as shown in Fig.1, the total electricity cost within a time period, which is an economic indicator to measure performance efficiency, is related to the

TOU tariff, the power of C4-C8, and the time period for investigation. The optimal scheduling is to minimize the energy cost subject to relevant constraints. Large ramp rates of belt speed do harm to certain equipment or components of the belt conveyor, consequently, it is necessary to form the constraints of speed ramp rates.

One way to reduce the ramp rates is integrating them into the objective function for minimization.

Other constraints for this optimal scheduling problem are listed as follows.

    1)         Because C4-C8 are serially interlinked and there are not buffers between them, the feed rates of C4-C8 should be the same at any time

    2)      At any time, the total amount of the coal in the 12 bins is within the range between HL and LL   
    3)      The total amount of coal fed to the bins is greater than or equal to the total consumption of the two units
   4)      At any time, the belt speeds of C4-C8 are within the feasible domain
   5)      Further, the feed rates of C4-C8 are within the feasible domain at any time. For each belt conveyor under investigation, the unit mass of the material on the belt, QG, should be less than its maximum value The coal consumption rate of the two units is needed

during the formulation of constraint In fact, the coal consumption of an unit can be forecasted through its load and inherent characteristics; and the load of an unit is further determined by economic dispatch. The coal consumption can be represented as a quadratic function of the unit load as follows [21]

                             (Pd) = aP²d+bPd+C 
where Pd is the load assign of the unit (MW), F(Pd) is the consumption rate t/h, and the three coefficients a, b & c are determined by inherent characteristics of the unit. In this case study, the two units are same model and from the same manufacturer. They are supposed to have the same function of coal consumption with 
a= 4.045x10^-5, b=0.3994, and c=12.02.
These coefficients are derived from the specification of the power plant.

The cumulative energy cost and the cumulative energy consumption of the two strategies with

 T =900 t/h respectively. We take the current control strategy as the baseline. It is found from this case study that the optimal scheduling reduces the energy cost dramatically by 33.039% and saves 15.977% of the energy consumption. Most of the cost reduction comes from the coordination of the TOU tariff and the working time of the belt conveyors and the left part is from the energy saving. The energy saving achieved by scheduling the feed rate and belt speed of each belt conveyor to keep its QG near the maximum value, QG max. with Tp=1500t/h, optimal sheduling strategy achieves 33.52% of the energy cost reduction and saves 7.072% of the energy consumption as well. In fact, the rated feed rates of C4-C8 (1500 t/h) do not coordinate their rated belt speeds optimally. This is why the energy saving can be achieved through the optimization of the feed rates and belt speeds of C4-C8 when they run with rated feed rates. Furthermore, it is clearly shown in Tab.III that the optimal scheduling strategy achieves more energy saving when it is applied to the cases with further limited feed rates, for these cases are farther from the optimal operation condition of the feeding process. In other words, a belt conveyor system with further limited feed rates has larger potential to improve its operation efficiency.

CONCLUSION

The energy efficiency improvement of a belt conveyor system can generally be achieved through any one of its four components (performance, operation, equipment, and (technology). This paper focuses on the most practical part, operation efficiency. An optimal scheduling is proposed to improve the operation efficiency of the belt conveyor system.

It integrates the energy model of belt conveyors, the TOU tariff, and ramp rates of belt speed into an objective function and takes other system and external constraints into consideration.

The operation efficiency of belt conveyor systems is improved by optimally scheduled operational instructions concerning the working time, belt speeds, and feed rates. A coal conveying system in coal-fired power plant is used for a case study where great reduction of energy cost and energy consumption are achieved. The energy consumption reduction, while making financial sense, makes it a sustainable scheme for energy management. Furthermore, a conclusion can be drawn that the belt conveyors with further limited feed rates have the larger potential to improve their operation efficiency. The presented optimal scheduling for

belt conveyors is formulated as a general optimal control problem, hence, it can be easily applied to other conveying systems or similar industrial application areas.

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