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
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.
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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