<p>Energy
management has become a major concern in the past two decades with the
increasing energy prices, overutilization of natural resources and increased carbon
emissions. According to the department of Energy the industrial sector solely
consumes 22.4% of the energy produced in the country [1]. This calls for an
urgent need for the industries to design and implement energy efficient
practices by analyzing the energy consumption, electricity data and making use
of energy efficient equipment. Although, utility companies are providing
incentives to consumer participating in Demand Response programs, there isn’t
an active implementation of energy management principles from the consumer’s
side. Technological advancements in controls, automation, optimization and big
data can be harnessed to achieve this which in other words is referred to as
“Smart Manufacturing”. In this research energy management techniques have been
designed for two SEU (Significant Energy Use) equipment HVAC systems,
Compressors and load shifting in manufacturing environments using control and
optimization.</p>
<p>The
addressed energy management techniques associated with each of the SEUs are
very generic in nature which make them applicable for most of the industries.
Firstly, the loads or the energy consuming equipment has been categorized into
flexible and non-flexible loads based on their priority level and flexibility
in running schedule. For the flexible loads, an optimal load scheduler has been
modelled using Mixed Integer Linear Programming (MILP) method that find carries
out load shifting by using the predicted demand of the rest of the plant and
scheduling the loads during the low demand periods. The cases of interruptible
loads and non-interruptible have been solved to demonstrate load shifting. This
essentially resulted in lowering the peak demand and hence cost savings for
both “Time-of-Use” and Demand based
price schemes. </p>
<p>The
compressor load sharing problem was next considered for optimal distribution of
loads among VFD equipped compressors running in parallel to meet the demand.
The model is based on MILP problem and case studies was carried out for heavy
duty (>10HP) and light duty compressors (<=10HP). Using the compressor
scheduler, there was about 16% energy and cost saving for the light duty
compressors and 14.6% for the heavy duty compressors</p>
<p>HVAC
systems being one of the major energy consumer in manufacturing industries was
modelled using the generic lumped parameter method. An Electroplating facility
named Electro-Spec was modelled in Simulink and was validated using the real
data that was collected from the facility. The Mean Absolute Error (MAE) was
about 0.39 for the model which is suitable for implementing controllers for the
purpose of energy management. MATLAB and Simulink were used to design and
implement the state-of-the-art Model Predictive Control for the purpose of
energy efficient control. The MPC was chosen due to its ability to easily
handle Multi Input Multi Output Systems, system constraints and its optimal
nature. The MPC resulted in a temperature response with a rise time of 10
minutes and a steady state error of less than 0.001. Also from the input
response, it was observed that the MPC provided just enough input for the
temperature to stay at the set point and as a result led to about 27.6% energy
and cost savings. Thus this research has a potential of energy and cost savings
and can be readily applied to most of the manufacturing industries that use
HVAC, Compressors and machines as their primary energy consumer.</p><br>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/8286359 |
| Date | 16 October 2019 |
| Creators | Harsha Naga Teja Nimmala (6849257) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Smart_Manufacturing_Using_Control_and_Optimization/8286359 |
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