| dc.identifier.uri | http://hdl.handle.net/11401/76432 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Current manufacturing facilities lack proper performance indicators that can accurately pinpoint areas of energy inefficiency on the manufacturing line. In studying the production dynamics, it is realized that not every disruption event causes permanent production loss for the manufacturing line. Each downtime event is categorized as non-effective (not causing permanent production loss) and effective (causing permanent production loss). This directly leads to the concept of the opportunity window, which is the largest amount of time a machine can be turned off without causing the slowest machine to become blocked or starved. The recovery time is also analyzed, which is the amount of time it takes the system to recover back to the original state after a downtime event. The opportunity window and recovery time are proven to be constant for a given line configuration in a deterministic scenario. Building upon the study of the production dynamics, this dissertation establishes new indices that utilize readily available, real sensor information from the production line, such as buffer levels, machine throughput, etc., to find the machine that is the least energy efficient. This is the machine that causes the line to waste the most energy without producing parts. The energy structure of the production line is analyzed to better understand the complex system dynamics and to find the root cause of the energy inefficiencies. A baseline energy consumption is established, which is the least amount of energy that is needed to produce a certain number of parts on the line. Using this knowledge, the new sustainable manufacturing performance indicators are defined to properly monitor the performance of the line. These indices utilize the energy structure to illustrate the static (energy used when there are no downtime events) and dynamic (energy lost due to downtime) portions of the energy consumption. The static portion of the energy structure is broken down further to find the optimal minimum energy consumption per part. This is a virtual scenario where each machine runs as a standalone machine and has no energy wasted due to unnecessary machine interactions. This provides plant managers with a quantitative self-benchmark for measuring system performance. The concepts of the downtime energy bottleneck and the rated power bottleneck are introduced and proven analytically. The downtime energy bottleneck is the machine that leads to the biggest energy waste reduction when prioritized for reactive maintenance. The rated power bottleneck is the machine, which when replaced, leads to the largest reduction in energy waste. These methods are verified using simulation studies in Simulink/MATLAB. Furthering the study of the energy indices, the production line dynamics are analyzed from an energy economics point of view. A return on investment strategy is introduced, which allows for the largest return on investment when replacing a machine or part with a more energy efficient version. The energy profit bottleneck is defined as the machine that leads to the largest increase in profit when prioritized for reactive maintenance. These concepts are used with an opportunity window control scheme to maximize overall profit in the manufacturing facility. The production line dynamics are integrated with the heating, ventilation, and air conditioning system to further reduce overall energy demand of the manufacturing plant. By merging the two largest energy consumers within the facility, we shift electrical demand to minimize the energy costs. The cooling load is analyzed to reduce the overall effect of the production system on the HVAC system. The opportunity window control methodology is utilized to further reduce electricity costs with minimal throughput impact on the production. In the future, an optimal control methodology will be developed that utilizes Markov decision process to maximize profits, while minimizing energy demand. The issue of data uncertainty will be addressed by introducing a Kalman Filter into the system. | |
| dcterms.available | 2017-09-20T16:50:15Z | |
| dcterms.contributor | Kao, Imin | en_US |
| dcterms.contributor | Chang, Qing | en_US |
| dcterms.contributor | Chen, Shikui | en_US |
| dcterms.contributor | Arinez, Jorge. | en_US |
| dcterms.creator | Brundage, Michael P. | |
| dcterms.dateAccepted | 2017-09-20T16:50:15Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:15Z | |
| dcterms.description | Department of Mechanical Engineering. | en_US |
| dcterms.extent | 147 pg. | en_US |
| dcterms.format | Monograph | |
| dcterms.format | Application/PDF | en_US |
| dcterms.identifier | http://hdl.handle.net/11401/76432 | |
| dcterms.issued | 2015-12-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:15Z (GMT). No. of bitstreams: 1
Brundage_grad.sunysb_0771E_12430.pdf: 2695044 bytes, checksum: 50019f98cd67d094456f566df7cad6b7 (MD5)
Previous issue date: 1 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Mechanical engineering | |
| dcterms.subject | Bottleneck Mitigation, Energy Analytics, Energy Economics, Integrated Modeling | |
| dcterms.title | Utilization of Energy Analytics to Reduce Energy Waste in the Manufacturing Environment | |
| dcterms.type | Dissertation | |
