Nawaz Ahmad, Kumar Pradeep
Department of Chemical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi 221005, India.
J Taiwan Inst Chem Eng. 2022 Oct;139:104538. doi: 10.1016/j.jtice.2022.104538. Epub 2022 Sep 28.
Nowadays, wearing a 3-layered face mask (3LFM) to protect against coronavirus illness (COVID-19) has become commonplace, resulting in massive, hazardous solid waste. Since most of them are infected with viruses, a secure way of disposal is necessary to prevent further virus spread. Pyrolysis treatment has recently developed as an effective method for disposing of such hazardous waste and consequently converting them into energy products. In this regard, the goal of the present study is to physicochemically characterize the 3LFM followed by pyrolysis in a TGA to evaluate the pyrolysis performance, kinetic, and thermodynamic parameters and in a semi-batch reactor to characterize the volatile product. Furthermore, an artificial neural network (ANN) was used to forecast thermal deterioration data. The results demonstrated a strong correlation between real and anticipated values. The study proved the relevance of the ANN model and the applicability of pyrolysis for disposing of 3LFM while simultaneously producing energy products.
如今,佩戴三层口罩(3LFM)来预防冠状病毒病(COVID-19)已变得司空见惯,这导致了大量有害固体废物的产生。由于大多数口罩都感染了病毒,因此需要一种安全的处理方式来防止病毒进一步传播。热解处理最近已发展成为一种处理此类有害废物并将其转化为能源产品的有效方法。在这方面,本研究的目的是对3LFM进行物理化学表征,然后在热重分析仪(TGA)中进行热解,以评估热解性能、动力学和热力学参数,并在半间歇反应器中对挥发性产物进行表征。此外,还使用了人工神经网络(ANN)来预测热降解数据。结果表明实际值与预测值之间具有很强的相关性。该研究证明了ANN模型的相关性以及热解在处理3LFM同时生产能源产品方面的适用性。