Valorisation of hazardous medical waste using steam injected plasma gasifier: a parametric study on the modelling and multi-objective optimisation by integrating Aspen plus with RSM

Protocol for the design and accelerated optimization of a waste-to-energy system using AI tools

Amid a surge in waste volume, the need to achieve sustainable waste treatment has become increasingly important. Here, we present a protocol for the design and accelerated optimization of a waste-to-energy system using artificial intelligence tools. We describe steps for waste treatment process advancement as demonstrated by the medical waste-to-methanol conversion and implementing data-driven process optimization. We then detail procedures for streamlining tasks by establishing connectivity between systems such as Aspen Plus and MATLAB. For complete details on the use and execution of this protocol, please refer to Shi et al. (2022)1 and Fang et al. (2022).2.

Conversion of medical waste into value-added products using a novel integrated system with tail gas treatment: Process design, optimization, and thermodynamic analysis

The COVID-19 pandemic has generated substantial medical waste (MW), posing risks to society. Based on widespread MW incineration, this study proposes an integrated system with tail gas treatment to convert MW into value-added products with nearly zero emissions. Herein, steam generators and supercritical CO₂ cycles were used to recover energy from MW to produce high-temperature/pressure steam and electricity. A simple power generation cycle achieved a net electricity efficiency of 22.4% through optimization. Thermodynamic analysis revealed that the most energy and exergy loss occurred in incineration. Furthermore, a pressurized reactive distillation column purified the resultant tail gas. The effects of inlet temperature, pressure, liquid/gas ratio, and recycle ratio on the removal and conversion efficiencies of NO₂ and SO₂ were evaluated. Nearly 100% of the SO₂ and 75% of the NO₂ generated by the incineration of MW have been converted into their acid forms. Based on the proposed tail gas treatment unit, high-purity CO₂ (∼98% purity) was finally obtained.

Virtual Methodology for Household Waste Characterization During The Pandemic in An Urban District of Peru: Citizen Science for Waste Management

The Covid-19 pandemic has caused the alteration of many aspects of the solid waste management chain, such as variations in the waste composition, generation and disposal. Various studies have examined these changes with analysis of integrated waste management strategies; qualitative studies on perceived variations and statistical evaluations based on waste collected or disposed in landfills. Despite this information there is a need for updated data on waste generation and composition, especially in developing countries. The objective of this article is to develop a data sampling and analytical approach for the collection of data on household waste generation and composition during the pandemic; and, in addition, estimate the daily generation of masks in the study area. The proposed methodology is based on the principles of citizen science and utilizes virtual tools to contact participants, and for the training and collection of information. The study participants collected the information, installed segregation bins in their homes and trained their relatives in waste segregation. The article presents the results of the application of the methodology in an urban district of Lima (Peru) in August 2020. The results suggest an apparent decrease in household waste per capita and a slight increase in plastics composition in the study area. It is estimated that each participant generates 0.124 masks per day and 0.085 pairs of gloves per day. The method developed and results presented can be used as a tool for public awareness and training on household waste characterization and segregation. Furthermore it can provide the necessary evidence to inform policy directives in response household waste issues and Covid-19 restrictions.