Municipal Solid Waste Gasification Process Research Articles

Gasification potential of municipal solid waste in Iran: Application of life cycle assessment, risk analysis, and machine learning

This paper focuses on a comprehensive solution for dry municipal solid waste (MSW) management with describes the scenario of the gasification process. For this purpose, the potential of implementing the MSW gasification process in Southeast Asia (Iran) with three agents of air, steam, and air-steam is investigated. In order to manage the MSW gasification process as accurately as possible in the study area, a thermodynamic, exergy, economic, environmental, carbon footprint, water footprint, ecological emergy, and risk assessment for the three mentioned gasification agents were carried out in the most populated cities of Iran. In the capital of Iran (Tehran) with the largest population and as a result of largest MSW production, the potential of the gasification process has been investigated at different times (10 consecutive years and 12 months of a year). Finally, by combining artificial neural networks and genetic programming, comprehensive relationships have been established to predict the thermodynamic conditions of gasification with an air-steam agent. The general results of the survey indicate that gasification with air agents has good potential for most regions. The use of the steam agent has created worse economic, environmental, and risk conditions than the other two agents. Investigating the potential of the gasification process in Tehran shows that this city has a good potential to implement this process compared to other cities in Iran, and the most suitable location for establishing the gasification process is the 4th region of this city.

Predicting municipal solid waste gasification using machine learning: A step toward sustainable regional planning

The gasification process can treat and valorize municipal solid waste (MSW) in an environmentally and economically friendly way. Using this process, MSW can be safely disposed of and sustainably converted into bioenergy as part of regional planning. Experimental laboratory data is a key component in designing, optimizing, controlling, and scaling up MSW gasifiers. However, most researchers lack the resources and time to conduct experiments. Machine learning (ML) technology can resolve this issue by detecting patterns and hidden information in published data. Hence, the present study aims to construct an inclusive ML model to predict and understand the MSW gasification process. The objective is to establish a consistent and homogeneous database containing MSW sources under different gasification conditions, followed by an analysis of the database using statistical methods. Three ML models are used to predict the distribution of syngas, char, and tar and the quality of syngas in MSW gasification using feedstock characteristics and gasification parameters. When a gradient boost regressor is used to model the process, the prediction accuracy is highest (R2 > 0.926, RMSE <6.318, and RRMSE <0.304). SHAP analysis is successfully used to understand the significance and contribution of descriptors on targets in the modeling process.

Seasonal exergoeconomic assessment and optimization of a dual-fuel trigeneration system of power, cooling, heating, and domestic hot water, proposed for Tabriz, Iran

Exergoeconomic assessment of a multigeneration system, which is fed with a mixture of natural gas and syngas produced from municipal solid waste gasification process, has been conducted under climatic restrictions of both hot and cold seasons. In the basic state of the 1st scenario, for the constant net power of 30 MW and 50% mixing ratio, exergy efficiency and total cost rate of the system are 27.92% and 1328 USD/h in summer, and 33.81% and 1246 USD/h, in winter, respectively. Moreover, the total relative cost difference in the summer is considerably higher than that in the winter. In the basic state of the 2nd scenario, for the constant fuel mass flow rate of 1.5 kg/s, the system's annual exergy efficiency, annual total cost rate, and payback period are 30.79%, 1166.4 USD/h, and 2 years, respectively. The optimization has been performed based on the genetic algorithm considering two objective functions. The optimization results revealed that, the system's annual exergy efficiency and annual total cost rate got improved from 30.79% to 32.37%, and from 1166.4 USD/h to 796.52 USD/h, respectively, while the payback period increases from 2 to 2.1 years. The complete results of parametric studies are also provided for both scenarios.

Methods for Producing Hydrogen-Rich Syngas in MSW Gasification

Although municipal solid waste (MSW) is a concern in many countries, it may be a highly appealing alternative feedstock for gasification to create sustainable gas fuels. The creation of hydrogen syngas from MSW gasification has received a lot of attention. The employment of a catalyst in the MSW gasification process is known to boost the gasification reactor's performance in producing hydrogen-rich syngas. Furthermore, the co-gasification approach is frequently utilized to increase syngas quality and gasification efficiency. This paper discusses many ways for gasifying MSW for the generation of hydrogen rich syngas. Catalytic gasification, co-gasification, and a modified gasification system are the gasification methods under consideration.

Scaling-up defect-free asymmetric hollow fiber membranes to produce oxygen-enriched gas for integration into municipal solid waste gasification process

Municipal solid waste (MSW) gasification is an attractive waste-to-energy (WTE) technology with promise for future solid waste management, owing to the production of syngas which can be used as an alternative energy resource. However, quality of produced syngas is not meeting expectation. One promising solution is to use oxygen-enriched gas (OEG) to elevate produced syngas quality but producing OEG is costly. Herein, we demonstrated an asymmetric defect-free hollow fiber membrane made up of Matrimid® 5218 polyimide for scaling-up to 2-inch membrane modules. Our modules have an effective surface area up to 2.6 m2 with a packing density of up to 44% to produce OEG of 45% O2 purity. The 2-inch membrane module was integrated into a lab-scale gasification process to produce syngas using refuse derived fuel (RDF) generated from MSWs collected from the university campus. Prior to this, the lab-scale gasification was first optimized by tuning the gasification temperature, O2 purity and equivalence ratio (ER). Then, MSW gasification using membrane-based OEG was carried out, and compared against air gasification and synthetic OEG of the same O2 purity. Our results showed that, at O2 purity of 45%, gasification temperature of 900 °C and ER of 0.15, the quality of produced syngas was elevated with a lower heating value (LHV) of 9.15 MJ/m3 and H2/CO ratio of 1.43, which was substantially higher than air gasification with LHV and H2/CO ratio reported at 5.38 MJ/m3 and 0.81, respectively. Gasification results for membrane-based OEG and synthetic OEG were also comparable, while process simulation evidence suggested that membrane-based separation was at least 2-fold less energy-intensive than cryogenic distillation and pressure swing adsorption. Overall, this proof-of-concept successfully demonstrates the viability and competitiveness of membrane process for producing OEG at medium O2 purity (i.e., 40–50%) to support MSW gasification for WTE conversion.

Synergistic effect, kinetic and thermodynamics parameters analyses of co-gasification of municipal solid waste and bituminous coal with CO2

Co-gasification of municipal solid waste (MSW) with bituminous coal (BC) is an attractive alternative to realize the harmless disposal and energy harvesting of MSW. In this work, co-gasification characteristics and synergistic interaction of MSW and BC with CO2 atmosphere are studied by thermogravimetric method, including analyses of thermodynamics, kinetic parameters and reaction mechanism function. Results indicate that MSW gasification process can be divided into four main stages, and that of BC has only three main stages. Gasification temperature of coal char is much higher than that of MSW char, and addition of MSW can significantly improve the gasification reactivity of BC. Besides, a significant synergistic effect is observed for all the blends in char gasification stage. Based on three kinetic methods of Flynn-Wall-Ozawa (Xie et al., 2018), Starink (Zhang et al., 2019a) and Friedman, the minimum average activation energy Ea (184.13 kJ/mol) is obtained when the blend ratio of BC is 40% which might be an optimal option for co-gasification of the blends. The average values of the enthalpy, the Gibbs function and the entropy changes for sample 60MSW40BC are 176.82 kJ/mol, 257.89 kJ/mol and −89.16 J/mol·K, respectively. According to the Malek method, F6, A1 and D7 models are probably more suitable to describe three main stages of sample 60MSW40BC CO2 co-gasification.

Production of hydrogen enriched syngas from municipal solid waste gasification with waste marble powder as a catalyst

Marble processing leads to the production of high amount of waste marble powder (WMP) as a byproduct, which can be a potential health risk and has hazardous impacts on the surrounding environment. However, marble is composed of calcite making it suitable for the calcium-based catalyst. Moreover, no study has been carried out to utilize this WMP in municipal solid waste (MSW) gasification process. Therefore, there is a need to address its utilization as a potential catalyst/sorbent in the gasification of municipal solid waste (MSW). A laboratory scale batch-type fixed bed reactor was used to study the effect of WMP addition on the CO2 adsorption, steam reforming capability and char gasification in the presence of steam. Produced gas composition, gas yield, carbon conversion efficiency and tar yield were examined at different WMP to MSW ratios. Effect of temperature and steam rate varying from 700 to 900 °C and 2.5–10 ml/min respectively were also considered in this study. WMP showed a good capacity towards hydrogen enriched syngas production as well as CO2 adsorption and tar reforming. The H2 concentration increased significantly with an increase in the WMP to MSW mass ratio, while CO2 decreased. A significant effect of temperature and steam rate was also observed on the produced gas composition, gas yield, and tar content. This study helps us to understand the effect of WMP addition in MSW gasification process and thus assists in the industrial application.

Experimental study on gasification performance of bamboo and PE from municipal solid waste in a bench-scale fixed bed reactor

Gasification performance of key components including polyethylene (PE) and bamboo of municipal solid waste (MSW) was examined in a bench-scale fixed bed. Effects of equivalence ratio, gasification temperature, steam/feedstock ratio, and calcium oxide (CaO) presence on syngas composition and lower heating value (LHV) were investigated. As equivalence ratio increased, both combustible gas components and LHV of syngas from bamboo and PE gasification decreased while the yield of CO2 increased generally. Higher gasification temperature favored improving H2 and CO production and lowering the yield of CO2 from PE gasification while an optimal temperature of 700°C existed for the best syngas quality and the highest LHV of syngas from bamboo gasification. Different variations of CO2 between bamboo and PE were observed as steam/feedstock ratio increased. CaO was more effective to increase the yields of H2, CO, and CH4 and lower the yield of CO2 from bamboo and PE gasification under both air and steam atmosphere, excluding the syngas composition of PE steam gasification. The work described here favors us understand the real MSW gasification process and thus facilitates the industrial application of gasification technology.

Multi-gene genetic programming based predictive models for municipal solid waste gasification in a fluidized bed gasifier

A multi-gene genetic programming technique is proposed as a new method to predict syngas yield production and the lower heating value for municipal solid waste gasification in a fluidized bed gasifier. The study shows that the predicted outputs of the municipal solid waste gasification process are in good agreement with the experimental dataset and also generalise well to validation (untrained) data. Published experimental datasets are used for model training and validation purposes. The results show the effectiveness of the genetic programming technique for solving complex nonlinear regression problems. The multi-gene genetic programming are also compared with a single-gene genetic programming model to show the relative merits and demerits of the technique. This study demonstrates that the genetic programming based data-driven modelling strategy can be a good candidate for developing models for other types of fuels as well.