The Experts below are selected from a list of 114804 Experts worldwide ranked by ideXlab platform
Christopher P Saint - One of the best experts on this subject based on the ideXlab platform.
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development of smart data analytics tools to support wastewater treatment Plant Operation
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: Christopher W K Chow, Jixue Liu, Nick Swain, Katherine Reid, Christopher P SaintAbstract:Abstract A case study of applying chemometrics approach, k-means, a clustering algorithm to develop a real-time industrial process early warning system using online measurements was conducted. An online spectrophotometer was installed for an eighteen-month monitoring study between 2013 and 2015 at the inlet of a wastewater treatment Plant. During this time a web-based prototype portal with data integration, visualization, prediction and anomaly detection functions for complex online data sets was developed in-house to assess the spectral data acquired by the spectrophotometer together with other databases (such as rainfall and temperature). Several chemometrics options, such as association analysis and feature selection, were used to extract useful Operational information from the acquired data. In this paper, the anomaly detection function which includes pattern learning and comparison algorithms and a powerful user interface was described in detail. By using the functions, process upsets were successfully detected from the spectral data at the inlet of the treatment Plant. The detected events/upsets were then compared with the treatment Plant logs and they were found aligned well, which proved that the anomaly detection technique was effective and has the potential to inform decision to assist Plant operators. In addition, the proposed anomaly detection technique is also a flexible algorithm which works with any similar time series data to detect other process related issues to provide real-time warning to support treatment Plant Operations.
Willi Gujer - One of the best experts on this subject based on the ideXlab platform.
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data driven modeling approaches to support wastewater treatment Plant Operation
Environmental Modelling and Software, 2012Co-Authors: David J Durrenmatt, Willi GujerAbstract:Data-driven modeling techniques are applied to process data from wastewater treatment Plants to provide valuable additional information for optimal Plant control. The application of data-driven modeling techniques, however, bears some risk because the generated models are of non-mechanistic nature and they thus do not always describe the Plant processes appropriately. In this study, a procedure to build software sensors based on sensor data available in the process information system is defined and used to compare several techniques suitable for data-driven modeling, including generalized least squares regression, artificial neural networks, self-organizing maps and random forests. Three different degrees of expert knowledge are defined and considered mainly for optimum input signal selection and model interpretation. In two full-scale experiments, software sensors are created. The experiments reveal that even with linear modeling techniques, it is possible to automatically generate accurate software sensors. Hence, this justifies the selection of the most parsimonious and transparent models and to motivate their investigation by taking into account available expert knowledge. A high degree of expert knowledge is valuable for long-term accuracy, but can lead to performance decreases in short-term predictions. With regard to safe on-site deployment, the consideration of uncertainty measures is crucial to prevent misinterpretation of software-sensor outputs in the cases of rare events or model input failures.
Christopher W K Chow - One of the best experts on this subject based on the ideXlab platform.
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development of smart data analytics tools to support wastewater treatment Plant Operation
Chemometrics and Intelligent Laboratory Systems, 2018Co-Authors: Christopher W K Chow, Jixue Liu, Nick Swain, Katherine Reid, Christopher P SaintAbstract:Abstract A case study of applying chemometrics approach, k-means, a clustering algorithm to develop a real-time industrial process early warning system using online measurements was conducted. An online spectrophotometer was installed for an eighteen-month monitoring study between 2013 and 2015 at the inlet of a wastewater treatment Plant. During this time a web-based prototype portal with data integration, visualization, prediction and anomaly detection functions for complex online data sets was developed in-house to assess the spectral data acquired by the spectrophotometer together with other databases (such as rainfall and temperature). Several chemometrics options, such as association analysis and feature selection, were used to extract useful Operational information from the acquired data. In this paper, the anomaly detection function which includes pattern learning and comparison algorithms and a powerful user interface was described in detail. By using the functions, process upsets were successfully detected from the spectral data at the inlet of the treatment Plant. The detected events/upsets were then compared with the treatment Plant logs and they were found aligned well, which proved that the anomaly detection technique was effective and has the potential to inform decision to assist Plant operators. In addition, the proposed anomaly detection technique is also a flexible algorithm which works with any similar time series data to detect other process related issues to provide real-time warning to support treatment Plant Operations.
Brigitte Werners - One of the best experts on this subject based on the ideXlab platform.
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optimal Operation of a chp Plant participating in the german electricity balancing and day ahead spot market
European Journal of Operational Research, 2017Co-Authors: Nadine Kumbartzky, Matthias Schacht, Katrin Schulz, Brigitte WernersAbstract:During the last years, operators of combined heat and power (CHP) Plants are challenged by changing circumstances in the electricity markets. While ensuring a stable heat and power supply for customers, CHP Plant operators seek to maximise the profitability of the CHP Plant Operation. We propose a comprehensive concept and illustrate its potential for increasing the profitability when operating a CHP Plant with heat storage by participating in multiple electricity markets. For this purpose, the structure of the decision-making process consisting of bid submission, market clearing and CHP Plant Operation planning is represented. A multistage stochastic mixed-integer linear programming (MILP) model is developed that simultaneously optimises the Operation of the CHP Plant with heat storage and bidding in sequential electricity markets. Price uncertainty is captured by means of stochastic processes. The proposed concept is applied to the real case of a municipal energy supply company that participates in the German day-ahead spot and balancing market. Results of the case study illustrate how trading in sequential electricity markets can result in an increased profitability of the CHP Plant Operation, where additional revenues from trading offset higher generation costs. Furthermore, we exemplify how the optimal Operation of the CHP Plant and heat storage device are adjusted according to revenue potential in multiple electricity markets.
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impact of heat storage capacity on chp unit commitment under power price uncertainties
A Quarterly Journal of Operations Research, 2016Co-Authors: Matthias Schacht, Brigitte WernersAbstract:Combined heat and power (CHP) Plants generate heat and power simultaneously leading to a higher efficiency than an isolated production. CHP unit commitment requires a complex Operation planning, since power is consumed in the moment of generation. The integration of a heat storage allows a partially power price oriented Plant Operation, where power is generated especially in times of high market prices. Consequently, an efficient Plant Operation depends on the accuracy of the anticipated power prices and the flexibility due to storage capacity. This contribution analyzes the effects of short-term uncertainties in power prices on the CHP unit commitment for different heat storage capacities. A simulation study is run to evaluate the financial impact of an inaccurate power price anticipation. Results show that the storage capacity affects the sensitivity of the solution due to stochastic influences. The isolated consideration of long-term uncertainties might result in a suboptimal choice of heat storage capacity. It is recommended, to explicitly consider short-term uncertainties when supporting strategic planning of heat storage capacities.
B W Hogg - One of the best experts on this subject based on the ideXlab platform.
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power Plant analyser a computer code for power Plant Operation studies
IEEE Transactions on Energy Conversion, 1996Co-Authors: B W HoggAbstract:This paper describes "Power Plant Analyser" (PPA), a computer code for power Plant dynamic and steady-state performance analysis. "Power Plant Analyser" simulates fossil power Plant systems-such as drum-type, once-through, gas turbine and combined cycle Plants-in a user-friendly manner. It provides a convenient tool for power engineers to understand the complex and interrelated thermodynamic processes and operating characteristics of the Plant. It can also be used for the conceptual training of power Plant operators, as well as a test bed for power Plant control and operating strategies.