No full-text available
To read the full-text of this research,
you can request a copy directly from the author.
Development of a Dust Violation Control Tool from Plant Data: SWEMP 2018—Selected Works
Abstract
Mining is an equipment-intensive industry that utilizes machinery both in production and mineral processing. As an essential part of sustainable development, mining is subject to environmental management from many aspects. The decision-making process integrated into all stages of mining has to be based on reliable data. Available technology enables to track and monitor the production stages in mining by using various sensors and systems. Data related to mining and mineral processing activities have different characteristics and therefore might be handled in different IT infrastructures. However, the integration of these different data infrastructures is of key importance for management. Mineral processing plant equipment is potentially a data source of process type of data, unique by its volume and frequency. Analyzing process type of data, such as sensors, is a challenging task for engineers that work in a dynamic work environment. Belt conveyors located in the mineral processing plants transport material between different stages such as crushing and grinding which are monitored by sensor systems. The data collected by these sensors is commonly visualized on SCADA screens and can provide real-time data about the operation. This study focuses on the available sensor data of belt conveyors in a mineral processing plant with an aim to manage the dust generated during material transportation. The belt conveyors and the water spraying systems are equipped with tags that provide data for daily management. A data integration tool was developed to create an alarm system to track whether the dust suppression systems were active during material was conveyed in the mineral processing plant. As a result, violations of dust suppression were identified, and the alarm system was integrated into the environmental management system of the operation.
ResearchGate has not been able to resolve any citations for this publication.
At EDF, a leading energy company, process data produced in power stations are archived both to comply with legal archiving requirements and to perform various analysis applications. Such data consist of timestamped measurements, retrieved for the most part from process data acquisition systems. After archival, past and current values are used for various applications, including device monitoring, maintenance assistance, decision support, statistics publication, etc.
Large amounts of data are generated in these power stations, and aggregated in soft real-time – without operational deadlines – at the plant level by local servers. For this long-term data archiving, EDF relies on data historians – like InfoPlus.21, PI or Wonderware Historian – for years. This is also true for other energy companies worldwide and, in general, industry based on automated processes.
In this paper, we aim at answering a simple, yet not so easy, question: how can data historians be placed in the data management landscape, from classical RDBMSs to NoSQL systems? To answer this question, we first give an overview of data historians, then discuss benchmarking these particular systems. Although many benchmarks are defined for conventional database management systems, none of them are appropriate for data historians. To establish a first objective basis for comparison, we therefore propose a simple benchmark inspired by EDF use cases, and give experimental results for data historians and DBMSs.
As the need of automation increases significantly, a control system needs to be easily programmable, flexible, reliable, robust and cost effective. In this paper a review on the application of programmable logic controller (PLC) in our current market is discussed. Investigations on the applications of PLCs in energy research, engineering studies, industrial control applications and monitoring of plants are reviewed in this paper. PLCs do have its own limitations, but findings indicate that PLCs have more advantages than limitations. This paper concludes that PLCs can be used for any applications whether it is of simple or complicated control system.
The Balanced Scorecard (BSC) translates an organization's mission and strategy into a comprehensive set of performance measures that provides the framework for a strategic measurement and management system. The scorecard measures organizational performance across four linked perspectives: financial, customer, internal business process, and learning and growth. (See Exhibit 1 on page 20.) The BSC enables companies to track short-term financial results while simultaneously monitoring their progress in building the capabilities and acquiring the intangible assets that generate growth for future financial performance.
We develop a compact and integrated methodological framework for Balanced Scorecard (BSC) synthesis and implementation. The proposed methodology capitalizes on existing knowledge, while incorporating critical issues, grounded in our research and experience, which have not been systematically considered or documented in previous work. By identifying shortcomings and critical success factors from literature and experience, the methodology aims at overcoming certain serious predicaments faced by many implementations. The methodology embodies activities related to Project Management, Change Management, Risk Management, Quality Assurance and Information Technology, areas that contribute considerably to BSC implementation success.
Development of an integrated asset management system for a metallurgical plant
- A Smit
Real-time integration of mining and metallurgical information for efficient use of energy and water
- O Bascur
- A Soudek