Simultaneous achievement of energy recovery and carbon sequestration through municipal solid waste management: A review

Effective municipal solid waste (MSW) management is a crucial component for sustainable cities, as inefficient waste disposal contributes to the release of about a billion tons of CO2-eq in greenhouse gases (GHG) annually. With escalating global waste generation, there is an untapped opportunity to integrate carbon dioxide removal (CDR) technologies into existing MSW management processes. This review explores current research on utilizing MSW for CDR, emphasizing its potential for both energy generation and carbon sequestration. The investigation covers three waste management practices: landfilling, waste-to-energy (WtE), and biochar production, revealing two paths for carbon sequestration. First, MSW serves as a feedstock in bioenergy with carbon capture and storage (BECCS), acting as a carbon-neutral resource that avoids fossil fuel and energy crop use, reducing GHG emissions and generating value through energy production. Second, direct storage of organic MSW and its derivatives, like biochar, in various carbon sinks allows for extended sequestration, offering a comprehensive approach to address the challenges of waste management and climate change mitigation. Moreover, this review advocates for an extended exploration into several subjects including in-depth analysis of waste, research on MSW-derived biochar recalcitrance across different carbon sinks, and understanding the symbiotic connections with GHG-emitting sectors like agriculture and energy. Finally, this review emphasizes the necessity of conducting life-cycle assessment studies to fully discern the benefits and assess the impacts of any future endeavors exploring the role of MSW in carbon sequestration.

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

The biomass higher heating value (HHV) is an important thermal property that determines the amount of recoverable energy from agriculture byproducts. Precise laboratory measurement or accurate prediction of the HHV is essential for designing biomass conversion equipment. The current study combines feature selection scenarios and machine learning tools to establish a general model for estimating biomass HHV. Multiple linear regression and Pearson's correlation coefficients justified that volatile matter, nitrogen, and oxygen content of biomass samples have a slight effect on the HHV and it is better to ignore them during the HHV modeling. Then, the prediction performance of random forest, multilayer and cascade feedforward neural networks, group method of data handling, and least-squares support vector regressor are compared to determine the intelligent estimator with the highest accuracy toward biomass HHV prediction. The ranking test shows that the multilayer perceptron neural network better predicts the HHV of 532 biomass samples than the other intelligent models. This model presents the outstanding absolute average relative error of 2.75% and 3.12% and regression coefficients of 0.9500 and 0.9418 in the learning and testing stages. The model performance is also superior to a recurrent neural network which was recently developed in the literature using the same databank.

Microbial astaxanthin: from bioprocessing to the market recognition

The attractive biological properties and health benefits of natural astaxanthin (AXT), including its antioxidant and anti-carcinogenic properties, have garnered significant attention from academia and industry seeking natural alternatives to synthetic products. AXT, a red ketocarotenoid, is mainly produced by yeast, microalgae, wild or genetically engineered bacteria. Unfortunately, the large fraction of AXT available in the global market is still obtained using non-environmentally friendly petrochemical-based products. Due to the consumers concerns about synthetic AXT, the market of microbial-AXT is expected to grow exponentially in succeeding years. This review provides a detailed discussion of AXT's bioprocessing technologies and applications as a natural alternative to synthetic counterparts. Additionally, we present, for the first time, a very comprehensive segmentation of the global AXT market and suggest research directions to improve microbial production using sustainable and environmentally friendly practices. KEY POINTS: • Unlock the power of microorganisms for high value AXT production. • Discover the secrets to cost-effective microbial AXT processing. • Uncover the future opportunities in the AXT market.

Simulating and Comparing CO2/CH4 Separation Performance of Membrane-Zeolite Contactors by Cascade Neural Networks

Separating carbon dioxide (CO₂) from gaseous streams released into the atmosphere is becoming critical due to its greenhouse effect. Membrane technology is one of the promising technologies for CO₂ capture. SAPO-34 filler was incorporated in polymeric media to synthesize mixed matrix membrane (MMM) and enhance the CO₂ separation performance of this process. Despite relatively extensive experimental studies, there are limited studies that cover the modeling aspects of CO₂ capture by MMMs. This research applies a special type of machine learning modeling scenario, namely, cascade neural networks (CNN), to simulate as well as compare the CO₂/CH₄ selectivity of a wide range of MMMs containing SAPO-34 zeolite. A combination of trial-and-error analysis and statistical accuracy monitoring has been applied to fine-tune the CNN topology. It was found that the CNN with a 4-11-1 topology has the highest accuracy for the modeling of the considered task. The designed CNN model is able to precisely predict the CO₂/CH₄ selectivity of seven different MMMs in a broad range of filler concentrations, pressures, and temperatures. The model predicts 118 actual measurements of CO₂/CH₄ selectivity with an outstanding accuracy (i.e., AARD = 2.92%, MSE = 1.55, R = 0.9964).

Carbon dioxide separation and capture by adsorption: a review

Rising adverse impact of climate change caused by anthropogenic activities is calling for advanced methods to reduce carbon dioxide emissions. Here, we review adsorption technologies for carbon dioxide capture with focus on materials, techniques, and processes, additive manufacturing, direct air capture, machine learning, life cycle assessment, commercialization and scale-up.

Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials

This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between -0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.

Employing computational fluid dynamics technique for analyzing the PACK-1300XY with methanol and isopropanol mixture

In this study, an innovative wire gauze structured packing, namely PACK-1300XY with a specific surface area of 1300 m²/m³ has been characterized by performing computational fluid dynamics (CFD) approach. Indeed, different features of this packing (height equivalent to a theoretical plate, wet/dry pressure drop, and mass transfer efficiency) were analyzed by analyzing the flow regime using the three-dimensional CFD approach with the Eulerian-Eulerian multiphase scenario. The results showed the mean relative deviation of 16% (for wet pressure drop), 14% (for dry pressure drop), and 17% (for mass transfer efficiency) between the CFD predictions and experimental measurements. These excellent levels of consistency between the numerical findings and experimental observations approve the usefulness of the CFD-based approach for reliable simulation of separation processes.

Applications of Biochar and Modified Biochar in Heavy Metal Contaminated Soil: A Descriptive Review

Given that the problem of contaminated soil continues to grow, the development of effective control and remediation measures has become imperative, especially for heavy-metal-contaminated soil. Biochar and modified biochar are eco-friendly and cost-effective remediation materials that are widely used in the remediation of contaminated soil. This review provides an overview of the different raw materials used in the preparation of biochar as well as the modification of biochar using various synthesis methods, highlighting their differences and providing recommendations for biochar and modified biochar as applied toward ameliorating pollution in soil contaminated by heavy metals. We also explore the effects of the physicochemical properties of raw materials, pyrolysis temperature, additives, and modification methods on the properties of the resulting biochar and modified biochar, and systematically present the types of soil and operating factors for repair. Moreover, the mechanisms involved in remediation of heavy-metal-contaminated soil by biochar and modified biochar are outlined in detail, and include adsorption, complexation, precipitation, ion exchange, and electrostatic attractions. Finally, the corresponding monitoring technologies after remediation are illustrated. Future directions for studies on biochar and modified biochar in the remediation of contaminated soil are also proposed to support the development of green environmental protection materials, simple preparation methods, and effective follow-up monitoring techniques.

Alternative Materials for the Enrichment of Biogas with Methane

Carbonaceous adsorbents have been pointed out as promising adsorbents for the recovery of methane from its mixture with carbon dioxide, including biogas. This is because of the fact that CO₂ is more strongly adsorbed and also diffuses faster compared to methane in these materials. Therefore, the present study aimed to test alternative carbonaceous materials for the gas separation process with the purpose of enriching biogas in biomethane and to compare them with the commercial one. Among them was coconut shell activated carbon (AC) as the adsorbent derived from bio-waste, rubber tire pyrolysis char (RPC) as a by-product of waste utilization technology, and carbon molecular sieve (CMS) as the commercial material. The breakthrough experiments were conducted using two mixtures, a methane-rich mixture (consisting of 75% CH₄ and 25% CO₂) and a carbon dioxide-rich mixture (containing 25% CH₄ and 75% CO₂). This investigation showed that the AC sample would be a better candidate material for the CH₄/CO₂ separation using a fixed-bed adsorption column than the commercial CMS sample. It is worth mentioning that due to its poorly developed micropore structure, the RPC sample exhibited limited adsorption capacity for both compounds, particularly for CO₂. However, it was observed that for the methane-rich mixture, it was possible to obtain an instantaneous concentration of around 93% CH₄. This indicates that there is still much potential for the use of the RPC, but this raw material needs further treatment. The Yoon–Nelson model was used to predict breakthrough curves for the experimental data. The results show that the data for the AC were best fitted with this model.

Catalysts Prepared with Matured Compost Derived from Mechanical-Biological Treatment Plants for the Wet Peroxide Oxidation of Pollutants with Different Lipophilicity

The current work proposes a strategy for the valorization of compost obtained from municipal solid waste, through its activation with H2SO4 and thermal treatment at 400–800 °C to produce low-cost catalysts for wet peroxide oxidation. All the developed materials were catalytically active for the aqueous solution oxidation of 2-nitrophenol (C2-NP,0 = 0.5 g L−1) and 4-nitrophenol (C4-NP,0 = 5.0 g L−1). In particular, using the catalysts prepared by thermal activation of the compost (with and without subsequent treatment with H2SO4), complete removal of both model pollutants was achieved after 24 h (at 50 °C, initial pH 3, Ccat = 2.5 g L−1, and employing a stoichiometric quantity of H2O2 needed for the total mineralization of the pollutants). In a cyclohexane–water mixture (simulating biphasic oily wastewater), 4-nitrophenol is also completely removed by the catalyst not treated with H2SO4. In contrast, the removal of 2-nitrophenol decreased to 93% due to its higher lipophilic character. Thus, this work demonstrates that active catalysts for wet peroxide oxidation can be obtained by using as a precursor a matured compost derived from municipal solid waste.