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Anaerobic digestion process modeling using Kohonen self-organising maps

机译：使用Kohonen自组织图的厌氧消化过程建模

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摘要

Anaerobic digestion is a versatile method for wastewater treatment as it not only reduces the waste but also leads to production of renewable energy. Modeling of the anaerobic process requires knowledge of biological and physico-chemical conditions, bacterial growth kinetics, substrate utilization, and product synthesis. However, the complexity of the process calls for highly sophisticated models requiring very high level of expertise and knowledge in the subject. This paper presents an approach for modeling of anaerobic digestion process through which the correlation between various process parameters can be studied, knowledge can be extracted, and system behaviour can be predicted. The datasets have been generated using a synthetic Matlab-Simulink-Excel model and process modelling is done using Kohonen Self organizing maps (KSOM). The resulting KSOM provided a visual interpretation of the inter-relationships between parameters (OLR, Sac, pH, Shco3, Q, Sglu_in, Qgas_out, Sglu_out, and Sch4_gas_out) which would help semi-skilled operators for operation and control of such plants. The model accurately predicts the variations in methane and total gas output with respect to changes in input parameters as the correlation is more than 90% for most of the parameters. This methodology offers a platform for scientists and researchers in comprehending the system behaviour under various operating conditions, even with missing data.
机译：厌氧消化是一种废水处理的通用方法，因为它不仅减少了废物，而且还产生了可再生能源。厌氧过程的建模需要了解生物学和物理化学条件，细菌生长动力学，底物利用率和产物合成。但是，过程的复杂性要求高度复杂的模型，需要在主题上具有很高水平的专业知识和知识。本文提出了一种厌氧消化过程建模的方法，通过该方法可以研究各种过程参数之间的相关性，可以提取知识并可以预测系统行为。使用合成的Matlab-Simulink-Excel模型生成数据集，并使用Kohonen自组织图（KSOM）进行过程建模。由此产生的KSOM直观地解释了参数（OLR，Sac，pH，Shco3，Q，Sglu_in，Qgas_out，Sglu_out和Sch4_gas_out）之间的相互关系，这将有助于半熟练的操作员对此类工厂进行操作和控制。该模型可准确预测甲烷和总气体输出相对于输入参数变化的变化，因为大多数参数的相关性均超过90％。这种方法为科学家和研究人员提供了一个平台，以了解在各种操作条件下甚至缺少数据时的系统行为。

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