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13 - Electrical and Electronic Engineering, Metallurgy and Materials

Queen's University Belfast

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Output 99 of 133 in the submission

Article title

Predicting organic acid concentration from UV/vis spectro measurements - a comparison of machine learning techniques

Type

D - Journal article

DOI

10.1177/0142331211403797

Title of journal

Transactions of the Institute of Measurement and Control

Article number

Volume number

Issue number

First page of article

ISSN of journal

0142-3312

Year of publication

2011

URL

Number of additional authors

Additional information

The paper describes research on an innovative new machine learning enabled approach to online indirect measurement of organic acid concentration in biogas plants using UV/vis spectroscopic probes. The research, undertaken in collaboration with the Gummersbach Environmental Computing Centre (GECO-C), University of Applied Sciences Cologne, for the first time makes online measurement of volatile fatty acids affordably available to small-to-medium scale biogas plants. The availability of VFA measurements greatly enhances process monitoring and affords new opportunities for biogas process control and optimisation. The proposed measurement system is currently being piloted at a bio-waste biogas plant in Northwest Germany.

Interdisciplinary

Cross-referral requested

Research group

C - Energy, Power and Intelligent Control (EPIC)

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract