D.T.J. O'Sullivan

Implementing the Green Batch: A case study: Continuous statistical evaluation to achieve the most energy efficient and reliable process

Manufacturers often experience different process performance from one batch to the next on the same equipment in the same process (e.g. energy use, lead time and downtime). In most cases, poor energy performance of a batch process is analysed retrospectively, and the root cause of the decline in performance/output must be manually identified In the concept of the Green Batch, process performance is continuously monitored against a best-performing batch. This paper proposes a methodology for implementing the Green Batch on an existing process.

A data access framework for integration to facilitate efficient building operation

Building Automation and Control Systems (BACs) are used to manage the day-to-day functions, operation and maintenance of a huge diversity of equipment within facilities of varying size and function. These systems are developed by a large number of hardware and software manufacturers who produce proprietary products designed to solve specific problems. As a result a number of different BACS can be operating within a single facility controlling various devices and producing significant quantities of data. This data can prove difficult to access due to the proprietary nature of the individual applications, thereby limiting the potential for a holistic view of a facilities operation and limiting the scope for big data analytics. This paper proposes the implementation of a Data Access Framework to coalesce the disparate information sources that can exist within a facility via OPC into a tagged database with particular focus on Heating Ventilation and Air Conditioning (HVAC) as a test case.

Results from testing of a “cloud based” automated fault detection and diagnosis tool for AHU's

Heating Ventilation and Air Conditioning (HVAC) system energy consumption, on average, accounts for 40% of an industrial sites total energy consumption. Studies have demonstrated that continuous commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHUs using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHUs across four commercial & industrial sites with over €104,000 annual energy savings detected by the AFDD tool.