<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>

<p> One primary focus on internal combustion engines is that these engines create multiple harmful exhaust gases that can cause damage to the environment. There are a number of advanced strategies that are currently being investigated to help reduce the amount of these harmful emissions that are emitted from IC engines. One such method of reducing harmful emission gases focuses on the three-way-catalyst. A three-way-catalyst (TWC) is an exhaust emission control device that is designed in such a way to take harmful exhaust gases and convert them into less harmful gases through various chemical reactions within the TWC. To help further the reduction of these harmful gases in the TWC, a novel two-loop control and estimation strategy is used. This control and estimation strategy involves the use of two loops with an inner-loop controller, outer-loop robust controller, and an estimator in the outer-loop. The estimator consists of a TWC model and an extended Kalman filter which is used to estimate the fractional oxidation state (FOS) of the TWC. This estimated FOS is then used by the robust controller, along with other parameters, to produce a desired engine lambda reference signal, λup. This desired lambda signal is then used by the inner-loop controller to control the engine lambda. Accurate control of lambda is important because the air-fuel-ratio range for a TWC to effectively achieve oxidation and reduction simultaneously is extremely narrow. Another primary focus in the field of internal combustion engines is designing and tuning advanced models within GT-Power that can accurately predict what will happen when running an actual engine. Designing, troubleshooting, and testing a GT-Power model is an extensive but rewarding process. Creating an accurate engine model can not only provide one with primary engine data that is also measurable in a test cell, but can also provide insight into some of the intricate processes and nature of the engine that are difficult or impossible to physically measure. Cummins has an extensive process of tuning GT-Power engine models. This process include items such as initial model calibrations, model discretizations, turbocharger tunings, and other items. Some of these processes are used to calibrate both Cummins Power Systems Business Unit engines as well as a Purdue B6.7N natural gas engine. </p>