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用于复杂工业工厂气体排放控制的新一代仪器化气味监测系统(IOMS)。

Next-generation of instrumental odour monitoring system (IOMS) for the gaseous emissions control in complex industrial plants.

机构信息

SEED - Sanitary Environmental Engineering Division, Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy; SPONGE Srl, Accademic Spin Off of the University of Salerno, Laboratory SEED, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy.

SARTEC, Saras Ricerche e Tecnologie Srl, I Traversa 2(a) Strada Est, Macchiareddu, Assemini, CA, Italy.

出版信息

Chemosphere. 2021 May;271:129768. doi: 10.1016/j.chemosphere.2021.129768. Epub 2021 Jan 23.

DOI:10.1016/j.chemosphere.2021.129768
PMID:33736228
Abstract

Odour emissions from complex industrial plants may cause potential impacts on the surrounding areas. Consequently, the validation of effective tools for the control of the associated environmental pressures, without hindering economic growth, is strongly needed. Nowadays, senso-instrumental methods by using Instrumental Odour Emissions Systems (IOMSs) is among the most attractive tool for the continuous monitoring of environmental odours, allowing the possibility of obtaining real-time information to support the decision-making process and proactive approach. The systems complexity and scarcity of real data limited their wider full-scale employment. The study presents an advanced prototype of IOMS for the continuous classification and quantification of the odours emitted in ambient air by complex industrial plants, to continuously control the plants emissions with backwards approach. The IOMS device was designed and optimized and included the system for the automatic control of the conditions inside the measurement chamber. The designed operational procedures were presented and discussed. Results highlighted the influence of temperature and air flow rate for the measurement repeatability. Accurate prediction model was created and optimized and resulted able to distinguish 3 different industrial odour sources with accuracy approximately equal to 96%. The models were optimized thanks to the software features, which allowed to automatically apply the designed statistical procedures on the identified dataset with different pre-processing approach. The usefulness of having a fully-developed and user-friendly flexible system that allowed to select and automatically compare different settings options, including the different feature extraction methods, was demonstrated in order to identify the best prediction model.

摘要

来自复杂工业工厂的气味排放可能对周围地区造成潜在影响。因此,强烈需要验证有效的工具来控制相关的环境压力,而不阻碍经济增长。如今,使用仪器气味排放系统(IOMS)的传感仪器方法是连续监测环境气味的最有吸引力的工具之一,允许获得实时信息以支持决策过程和主动方法。系统的复杂性和实际数据的稀缺性限制了它们更广泛的全面应用。本研究提出了一种用于连续分类和量化复杂工业工厂排放到环境空气中的气味的先进 IOMS 原型,以便通过回溯方法连续控制工厂排放。设计并优化了 IOMS 设备,包括测量室内自动控制条件的系统。提出并讨论了设计的操作程序。结果强调了温度和空气流速对测量重复性的影响。创建并优化了准确的预测模型,结果表明能够以大约 96%的准确度区分 3 种不同的工业气味源。由于软件功能,对模型进行了优化,这些功能允许在不同的预处理方法上自动将设计的统计程序应用于已识别的数据集。展示了拥有一个完全开发和用户友好的灵活系统的有用性,该系统允许选择和自动比较不同的设置选项,包括不同的特征提取方法,以确定最佳预测模型。

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Degradation of gaseous volatile organic compounds (VOCs) by a novel UV-ozone technology.新型紫外-臭氧技术对气态挥发性有机化合物(VOCs)的降解。
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Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant.
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