Recent advances in organic waste pyrolysis and gasification in a CO2 environment to value-added products.

The persistent combustion of fossil fuels has resulted in a widespread greenhouse effect attributable to the continual elevation of carbon dioxide (CO2) levels in the atmosphere. Recent research indicates that utilizing CO2 as a pyrolysis gasification medium diminishes CO2 emissions and concurrently augments the value of the resultant pyrolysis gasification products. This paper reviews recent advancements in the pyrolysis gasification of organic solid wastes under a CO2 atmosphere. Meanwhile, the mechanisms of CO2 influence in the pyrolysis and gasification processes were also discussed. In comparison to noble gases, CO2 exhibits reactivity with char at≥710 °C, resulting in additional mass loss of the sample. In addition, CO2 was able to increase the specific surface area and stability of biochar and reduce biooil toxicity by lowering the content of cyclic compounds in the biooil, while CO2 was able to react with GPRs with some volatile products (e.g., light hydrocarbons) to increase biogas yield. Finally, CO2 also prevents catalyst deactivation by reducing secondary coke formation. We also recommend directing future attention toward utilizing unpurified CO2 in pyrolysis and gasification. This review aims to expand the utilization of CO2 and advocate for applying pyrolysis gasification products.

Roles of red mud in remediation of contaminated soil in mining areas: Mechanisms, advances and perspectives.

Red mud (RM) is a kind of strong alkaline solid waste produced from the aluminum industry, which contributes significantly to environmental pollution and can cause severe health issues.Currently, RM is widely recognized as a potential material for soil remediation because of its rich metal oxide content, such as Fe/Al oxides. However, there is no comprehensive description on the roles of RM in passivation remediation of contaminated soil in mining areas. This review summarizes the mechanisms of passivation of heavy metals (HMs) in contaminated soil by RM, including precipitation, adsorption and ion exchange. Besides the effects of adding RM on soil physicochemical properties, heavy metal forms and ecological environment are further elaborated. Moreover, using the co-hydrothermal carbonization of RM and biomass for enhancing the efficiency of contaminated soil remediation is proposed as the main prospective research. This paper provides technical references for the resource utilization of RM and the treatment of heavy metal-contaminated soil.

Synergistic control potential of flue gas pollutants under Ultra-Low emission standards in waste incineration plants.

As the dominant waste disposal process, incineration is regarded as the main incentive for the "not-in-my-backyard" syndrome, and faces an inescapable pressures of ultra-low emissions (ULE). Establishing precise response relationships between emission factors (EFs) and full-process influencing factors can provide guidance for the synergistic mitigation of flue gas pollutants (FGPs). In this work, the multi-dimensional EFs of FGPs were identified by initially integrating FGPs concentration monitoring data of existing 1,226 processing lines in China, technologies applied and operational experience (OE), local economic and political characteristics. Significant regional imbalance performance was observed, which EFs in the coastal regions were 3.55-92.39 % lower than those of the inland areas. NOx, SO2, HCl were identified as critical components requiring further reduction under the ULE standards, with exceedance rates recorded at 73.07 %, 38.90 %, and 56.69 %, respectively. An indicative value of 20 mg/m3 for PM is recommended for the control of heavy metals of Cd + Tl and Sb + As + Pb + Cr + Co + Cu + Mn + Ni based on the correlation coefficients of r = 0.28 (p < 0.001) and r = 0.20 (p = 0.002), respectively. Waste composition and OE were quantified as the main contributors of EFs' disparities by the tree-branching controlled variable approach established in this study. Predictive models for FGPs control process and corresponding EFs were constructed. EFs of nine FGPs in 2030 would decrease by 0.97-65.42 %, due to more complex purification processes employed to meet ULE's limitations, such as the application of five-stage processes growing from 45.60 % to 58.28 %. While regional imbalance in EFs-SO2 and EFs-HCl were extended with increases from 25.83 % to 33.07 % and 9.91 % to 32.32 %, respectively, due to the consistent disparities of OE and growing heterogeneity of control policies. Enhancing interregional empirical exchanges, reducing the regional market monopolies, and formulating technical guidelines would be beneficial to synergize the reduction of FGPs emissions and alleviate regional imbalance.

Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach.

The escalating use of alicyclic compounds in modern industrial production has led to a rapid increase of these substances in the environment, posing significant health hazards. Addressing this challenge necessitates a comprehensive understanding of these compounds, which can be achieved through the deep learning approach. Graph neural networks (GNN) known for its' extraordinary ability to process graph data with rich relationships, have been employed in various molecular prediction tasks. In this study, alicyclic molecules screened from PCBA, Toxcast and Tox21 are made as general bioactivity and biological targets' activity prediction datasets. GNN-based models are trained on the two datasets, while the Attentive FP and PAGTN achieve best performance individually. In addition, alicyclic carbon atoms make the greatest contribution to biological activity, which indicate that the alicycle structures have significant impact on the carbon atoms' contribution. Moreover, there are terrific number of active molecules in other public datasets, indicates that alicyclic compounds deserve more attention in POPs control. This study uncovered deeper structural-activity relationships within these compounds, offering new perspectives and methodologies for academic research in the field.

Enhanced activation of peroxymonosulfate for advanced oxidation processes using solid waste: A novel and easy implement high-value utilization process of slag.

A simple, efficient and low energy-consuming process available to generate resultful radicals from PMS for organic pollutants removal had been employed in this study. Slag had been used as the activator for organic pollutants degradation under slag/PMS advanced oxidation process. In this work, effects of slag with or without pretreatment on pollutant removal were studied and radical species generated by slag were measured. Calcination pretreatment is one efficient method to enhance the degradation efficiency significantly. Due to Fe3O4 and Fe2O3 became the dominant phases after calcination, it was about 8.6-flods increasing after comparing the pollutant removal efficiency for different slag/PMS system with calcination pretreatment or not. Organic pollutant neither degraded in PMS system at 25 °C nor being absorbed by slag system for 60 min. On the contrary, up to 90% pollutant concentration reduction achieved in the slag/PMS process. During this process, both •OH and SO4•- had been detected once slag and PMS interaction in wastewater. Through the free radicals quenching tests,•OH should be the key free radical in this advanced oxidation process for the organic pollutant removal under this alkaline condition. In general, organic degradation rate was determined by the slag dosage, and the maximum degradation efficiency was mainly controlled by the PMS usage. This work is expected to broaden the high-value reutilization way for industrial solid waste.

Enhancement of anaerobic digestion from food waste via inert substances based on metagenomic analysis: Oxidative phosphorylation and metabolism.

The application of anaerobic digestion (AD) in the treatment of food waste (FW) has become widespread. However, the presence of inert substances, such as bones, ceramics, and shells, within FW introduces a degree of uncertainty into the AD process. To clarify this intricate issue, this study conducted an in-depth investigation into the influence of inert substances on AD. The results revealed that when inert substances were present at a concentration of 0.08 g/g VSS, methane productivity in the AD process was significantly augmented by 86%. Subsequent investigations suggested that this positive effect was primarily evident in various biochemical processes, including solubilization, hydrolysis acidification, methanogenesis, and the accumulation of extracellular polymeric substances. Metagenomic analysis showed that inert substances enhance the relative abundance of hydrolytic bacteria and have a pronounced impact on the relative abundance of hydrogenotrophic methanogens (Methanosarcina) and acetotrophic methanogens (Methanobacterium). Additionally, inert substances significantly increased the relative abundance of functional genes in oxidative phosphorylation, a pivotal pathway for ATP synthesis. Furthermore, inert substances had a substantial effect on the functional genes related to the metabolic pathways associated with methanogenesis (both hydrogenotrophic and acetotrophic). This comprehensive study shed light on the substantial impact of inert substances on the AD of food waste, contributing to an enhanced understanding of the underlying mechanisms of anaerobic fermentation.

Diagnosis of invasive pulmonary fungal infections by a real-time panfungal PCR assay in non-neutropenic patients.

This study explored the utility of quantitative real-time panfungal PCR assay in diagnosing invasive pulmonary fungal diseases (IPFD) in non-neutropenic patients. Panfungal PCR assay was performed on respiratory tract specimens from patients whose clinical signs could not exclude fungal infection. At the same time, the samples were subjected to bacterial and fungal culture, microscopic examination and galactomannan antigen (GM) test in order to find the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the 4 diagnostic methods in proven and probable cases. 518 specimens were collected while 63 respiratory tract specimens tested by PCR had positive results. According to diagnostic criteria, 40 patients were diagnosed with IPFD, with 12 proven, 20 probable and 8 possible cases. Among these, 33 patients of PCR results were positive, most of which were from BALF samples (44.12%). 23 cases were caused by Aspergillus species, with Aspergillus fumigatus was the major cause. Other Aspergillus species, including Aspergillus flavus, Aspergillus terreus and Aspergillus nidulans were found in 1 sample respectively. Candida species were found in 5 samples, Pneumocystis jeroveci pneumonia (PJP) in 4 samples and Mucormycosis in 1 sample. An analysis of proven/probable diagnosis showed a sensitivity of 78.13%, specificity of 92.18%, PPV of 39.68% and NPV of 98.46% for PCR and 50%, 85.27%, 35.7%, 95.65% for GM test respectively. The Ct value difference between proven/probable and possible cases had no statistical significance (P = .824). Fungal culture showed a sensitivity of 17.5% while microscopic examination sensitivity of 32.5%. Through stratified analysis, no apparent correlation was found between the Ct value of the PCR assay and GM value (r: 0.223, P = .294). But a conjunction of the 2 tests raised the PPV of Aspergillus to 90%. As shown in this study, the panfungal RT-PCR assay has high sensitivity and consistency with serological test and culture. Its high PPV in the detection of Aspergillus and PJP were also evident.

Data-driven models applying in household hazardous waste: Amount prediction and classification in Shanghai.

Precisely predicting the amount of household hazardous waste (HHW) and classifying it intelligently is crucial for effective city management. Although data-driven models have the potential to address these problems, there have been few studies utilizing this approach for HHW prediction and classification due to the scarcity of available data. To address this, the current study employed the prophet model to forecast HHW quantities based on the Integration of Two Networks systems in Shanghai. HHW classification was performed using HVGGNet structures, which were based on VGG and transfer learning. To expedite the process of finding the optimal global learning rate, the method of cyclical learning rate was adopted, thus avoiding the need for repeated testing. Results showed that the average rate of HHW generation was 0.1 g/person/day, with the most significant waste categories being fluorescent lamps (30.6 %), paint barrels (26.1 %), medicine (26.2 %), battery (15.8 %), thermometer (0.03 %), and others (1.22 %). Recovering rare earth element (18.85 kg), Cd (3064.10 kg), Hg (15643.43 kg), Zn (14239.07 kg), Ag (11805.81 kg), Ni (4956.64 kg) and Li (1081.45 kg) from HHW can help avoid groundwater pollution, soil contamination and air pollution. HVGGNet-11 demonstrated 90.5 % precision and was deemed most suitable for HHW sorting. Furthermore, the prophet model predicted that HHW in Shanghai would increase from 794.43 t in 2020 to 2049.67 t in 2025.

Transformer-based enhanced model for accurate prediction and comprehensive analysis of hazardous waste generation in Shanghai: Implications for sustainable waste management strategies.

The escalating generation of hazardous waste (HW) has become a pressing concern worldwide, straining waste management systems and posing significant health hazards. Addressing this challenge necessitates an accurate understanding of HW generation, which can be achieved through the application of advanced models. The Transformer model, known for its ability to capture complex nonlinear processes, proves invaluable in extracting essential features and making precise HW generation predictions. To enhance comprehension of the key factors influencing HW generation, visualization techniques such as SHapley Additive exPlanations (SHAP) provide insightful explanations. In this study, a novel approach combining classical deep learning algorithms with the Transformer model is proposed, yielding impressive results with an R2 value of 0.953 and an RMSE of 7.284 for HW prediction. Notably, among the five key fields considered-demographics, socio-economics, industrial production, environmental governance, and medical health-industrial production emerges as the primary contributor, accounting for over 50% of HW generation. Moreover, a high rate of industrial development is anticipated to further accelerate this process.

Hi-MGT: A hybrid molecule graph transformer for toxicity identification.

Conventional toxicity testing methods that rely on animal experimentation are resource-intensive, time-consuming, and ethically controversial. Therefore, the development of alternative non-animal testing approaches is crucial. This study proposes a novel hybrid graph transformer architecture, termed Hi-MGT, for the toxicity identification. An innovative aggregation strategy, referred to as GNN-GT combination, enables Hi-MGT to simultaneously and comprehensively aggregate local and global structural information of molecules, thus elucidating more informative toxicity information hidden in molecule graphs. The results show that the state-of-the-art model outperforms current baseline CML and DL models on a diverse range of toxicity endpoints and is even comparable to large-scale pretrained GNNs with geometry enhancement. Additionally, the impact of hyperparameters on model performance is investigated, and a systematic ablation study is conducted to demonstrate the effectiveness of the GNN-GT combination. Moreover, this study provides valuable insights into the learning process on molecules and proposes a novel similarity-based method for toxic site detection, which could potentially facilitate toxicity identification and analysis. Overall, the Hi-MGT model represents a significant advancement in the development of alternative non-animal testing approaches for toxicity identification, with promising implications for enhancing human safety in the use of chemical compounds.

Minute pulmonary meningothelial-like nodules: associations between computed tomography and pathology features.

Background: Increased use of multislice computed tomography (CT) scans has revealed that minute pulmonary meningothelial-like nodules (MPMNs) showed as ground-glass nodules (GGNs) are frequent in patients. However, little is known about the incidence and fate of nodules. By using a cross-sectional design, this study compared the multislice CT signs and pathological results of MPMNs, and further used pathological results to explain the formation mechanism of the CT signs of MPMNs to improve the clinical understanding of the disease. Methods: The clinicopathological data of 93 cases diagnosed as MPMNs in the Jiangsu Province Hospital from January 2016 to September 2019 and the Nanjing First Hospital from January 2017 to December 2019 were examined. The related literature was reviewed, and each case's age, gender, medical history, and preoperative CT examinations were classified. Based on CT signs, this study analyzed the imaging features, including size, shape, boundary, distribution, opacity, and their relationship with pulmonary blood vessels. Results: A total of 13 cases had immunohistochemistry results among which the lesions showed consistent positive expression of vimentin (100%), followed by epithelial cell membrane antigen (92.3%) and progesterone (8%). The MPMNs mainly occurred in individuals aged 50-59 years (32.6%). Most patients (82.6%) had neoplastic disease. All nodules (100%) manifested with a round shape and well-demarcated borders on images. The size of the nodules on CT scans ranged from 2.5 to 5.0 mm, with an average size of 3.04±1.12 mm. Most nodules were subpleural (89.1%) and showed ground-glass opacity (97.8%). The follow-up results of postoperative clinical manifestations and chest CT examination were negative in 12 patients. Conclusions: This study suggested that the pathological findings of MPMNs could explain the formation mechanism of the CT signs. The results can provide guidance for the diagnosis of the disease in the future.

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal solid waste sorting.

An intelligent and efficient methodology is needed owning to the continuous increase of global municipal solid waste (MSW). This is because the common methods of manual and semi-mechanical screenings not only consume large amount of manpower and material resources but also accelerate virus community transmission. As the categories of MSW are diverse considering their compositions, chemical reactions, and processing procedures, etc., resulting in low efficiencies in MSW sorting using the traditional methods. Deep machine learning can help MSW sorting becoming into a smarter and more efficient mode. This study for the first time applied MSWNet in MSW sorting, a ResNet-50 with transfer learning. The method of cyclical learning rate was taken to avoid blind finding, and tests were repeated until accidentally encountering a good value. Measures of visualization were also considered to make the MSWNet model more transparent and accountable. Results showed transfer learning enhanced the efficiency of training time (from 741 s to 598.5 s), and improved the accuracy of recognition performance (from 88.50% to 93.50%); MSWNet showed a better performance in MSW classsification in terms of sensitivity (93.50%), precision (93.40%), F1-score (93.40%), accuracy (93.50%) and AUC (92.00%). The findings of this study can be taken as a reference for building the model MSW classification by deep learning, quantifying a suitable learning rate, and changing the data from high dimensions to two dimensions. Electronic Supplementary material: Supplementary material is available in the online version of this article at 10.1007/s11783-023-1677-1 and is accessible for authorized users.

Highly-efficient molten NaOH-KOH for organochlorine destruction: Performance and mechanism.

Molten salt has been increasingly acknowledged to be useful in the destruction of chlorine-containing organic wastes (COWs), e.g., organochlorine. However, the operational temperatures are usually high, and local structure and thermodynamic property of the molten salt remain largely unclear. In this study, novel molten NaOH-KOH is developed for organochlorine destruction, and its eutectic point can be lowered to 453 K with 1:1 mol ratio of NaOH to KOH. Further experiment shows that this molten NaOH-KOH is highly-efficient towards the destructions of both trichlorobenzene and dichlorophenol, acquiring the final dechlorination efficiencies as 88.2% and 94.1%, respectively. The organochlorine destruction and chloride salt enrichment are verified by fourier-transform infrared spectrometer. Molten NaOH-KOH not only eliminates the C-Cl and CC bonds, but also traps generated CO2, other acidic gases, and possibly particulate matters as a result of the high surface area and high viscosity. This makes it possibly advantageous over incineration for organic waste destruction for carbon neutrality. To sufficiently reveal the inherent mechanism for the temperature dependent performance, molecular dynamics simulation is further adopted. Results show that the radial distance between ions increases with temperature, causing larger molar volume and lower resistance to shear deformation. Moreover, thermal expansion coefficient, specific heat capacity, and ion self-diffusion coefficient of the molten NaOH-KOH are found to increase linearly with temperature. All these microscopic alterations contribute to the organochlorine destruction. This study benefits to develop highly-efficient molten system for COWs treatment via a low-carbon approach.

Pyrolytic kinetics, reaction mechanisms and gas emissions of waste automotive paint sludge via TG-FTIR and Py-GC/MS.

The present study experimentally quantified the pyrolysis behaviors of waste solvent-based automotive paint sludge (OAPS) and water-based automotive paint sludge (WAPS) at four different heating rates using thermogravimetric-Fourier transform infrared (TG-FTIR) spectrometry and pyrolysis-gas chromatography-mass (Py-GC/MS) spectrometry analyses. Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) methods combined with the master-plots method were employed to investigate the pyrolysis kinetics and reaction mechanisms of waste automotive paint sludge. Three reaction stages and three reaction peaks in stage 2 were distinguished for both OAPS and WAPS degradation. The average activation energy (Ea) estimates for OAPS (FWO: 179.09 kJ/mol; KAS: 168.28 kJ/mol) were slightly higher than WAPS (FWO: 175.90 kJ/mol; KAS: 164.80 kJ/mol) according to FWO and KAS methods. The main pyrolysis reaction mechanisms of both OAPS and WAPS closely matched with the order-based model corresponding to 3rd and 2nd order random nucleation on an individual particle. The evolved gas species of CH4, CO2, phenols, NH3, H2O, and CO from OAPS and WAPS pyrolysis were identified by TG-FTIR. According to Py-GC/MS, hydrocarbons (47.2%) and O-components (42.7%) were relatively large after OAPS and WAPS pyrolysis, respectively. Melamine was the most abundant N-component product after pyrolysis of OAPS (5.8%) and WAPS (4.8%).

Agglomeration-influenced transformation of heavy metals in gas-solid phases during simulated sewage sludge co-incineration: Effects of phosphorus and operating temperature.

Phosphorus and operating temperature not only affect the agglomeration behavior but also the transformation and migration of heavy metals. Accordingly, this study examined the effect of temperature and phosphorus in a fluidized bed combustion process to understand the emission and distribution of heavy metals by both experimental and thermodynamic calculations. The experimental results indicated that the sodium-phosphate reactions occur before the sodium-silicate reaction in the solid phase when the ratio of P/Na was 1/2. A low-melting-point sodium phosphate component, such as NaPO3, leads to easier particle agglomeration than Na2O-SiO2. In terms of the emissions of heavy metals, Pb and Cd show a similar trend: both the amount of emission smaller than that without adding phosphorus and the amount of emission share an upward trend with the operating time increased during MSS fluidized bed combustion. However, with the presence of phosphorus, the emission of Cr shows slightly decreased, and then sharply dropped, after that, increasing with operating time increased. Generally speaking, the maximum amount of Pb and Cd emitted was at 900 °C, followed by 800 °C and 700 °C. The higher temperature would promote the volatilization of Pb and Cd to emit. On the other hand, Cr emitted at the beginning tended to increase but later decreases when the temperatures were 700 and 900 °C, which may be due to the emission of Cr being influenced by the different affinities of both Al and Cr, reacting with Na in a fluidized bed incinerator. As for the distribution of heavy metals in the solid phase, a higher concentration of heavy metals was found in both the coarsest and finest particles during the process of agglomeration/defluidization.

MIST: multiple instance learning network based on Swin Transformer for whole slide image classification of colorectal adenomas.

Colorectal adenoma is a recognized precancerous lesion of colorectal cancer (CRC), and at least 80% of colorectal cancers are malignantly transformed from it. Therefore, it is essential to distinguish benign from malignant adenomas in the early screening of colorectal cancer. Many deep learning computational pathology studies based on whole slide images (WSIs) have been proposed. Most approaches require manual annotation of lesion regions on WSIs, which is time-consuming and labor-intensive. This study proposes a new approach, MIST - Multiple Instance learning network based on the Swin Transformer, which can accurately classify colorectal adenoma WSIs only with slide-level labels. MIST uses the Swin Transformer as the backbone to extract features of images through self-supervised contrastive learning and uses a dual-stream multiple instance learning network to predict the class of slides. We trained and validated MIST on 666 WSIs collected from 480 colorectal adenoma patients in the Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School. These slides contained six common types of colorectal adenomas. The accuracy of external validation on 273 newly collected WSIs from Nanjing First Hospital was 0.784, which was superior to the existing methods and reached a level comparable to that of the local pathologist's accuracy of 0.806. Finally, we analyzed the interpretability of MIST and observed that the lesion areas of interest in MIST were generally consistent with those of interest to local pathologists. In conclusion, MIST is a low-burden, interpretable, and effective approach that can be used in colorectal cancer screening and may lead to a potential reduction in the mortality of CRC patients by assisting clinicians in the decision-making process. © 2022 The Pathological Society of Great Britain and Ireland.

The relationship of serum gastrin-17 and oral mucositis in head and neck carcinoma patients receiving radiotherapy.

OBJECTIVE: The aim of this study was to analyze the relationship of serum gastrin-17 (G-17) and oral mucositis in head and neck carcinoma (HNC) patients receiving radiotherapy. METHODS: Serum G-17 were detected in patients before and after radiotherapy. Patients were divided into high G-17 group (baseline serum G-17 ≥ 5pmol/L) and low G-17 group (baseline serum G-17 < 5pmol/L). The severity of oral mucositis was analyzed between the two groups. Other complications such as dysphagia, salivary gland, mandible, thyroid function, larynx, pain, and weight loss were also investigated. RESULTS: Forty-two patients were analyzed in this study. The level of serum G-17 had a significant decrease after radiotherapy (7.29 ± 5.70pmol/L versus 4.93 ± 4.46pmol/L, P = 0.038). In low serum G-17 group, the incidences of grade 0, 1-2 and 3-4 of oral mucositis were 0%, 30.4%, and 69.6%, respectively. In high serum G-17 group, the incidences of grade 0, 1-2 and 3-4 of oral mucositis were 0%, 63.2%, and 36.8%, respectively. Pearson correlation analysis showed that serum G-17 was negatively correlated with oral mucositis (r=-0.595, P < 0.01). Weight loss of low G-17 group was more serious than that of high G-17 group. CONCLUSION: Serum G-17 has a close relationship with oral mucositis. Baseline serum G-17 may be a potential predictor for the severity of oral mucositis in HNC patients receiving radiotherapy.

Incineration disposal of organic waste bio-residue via a deep dewatering process using refuse incineration bottom ash: moisture transfer and low calorific value improvement.

Bio-residue is a by-product from organic waste anaerobic digestion process with high moisture, high organic matter, low calorific value and poor biological stability, and may be incinerated after deep dewatering. The moisture existence forms of bio-residue were clarified firstly, finding that adsorbed water, capillary water and bound water occupied 75.61%, 23.81% and 0.58%, respectively. Furthermore, refuse incineration bottom ash was used as a typical deep dewatering reagent, when compared with calcium oxide (CaO), refuse incineration fly ash and iron powder-potassium persulfate (Fe(0)-K2S2O8). Results showed that the addition of bottom ash powder could decrease the bio-residue's moisture from 80.0% to 37.9% at the optimal dosage of 2.0%, and corresponding low calorific value reached 10.5 MJ/kg at the 10th d. It was suggested that ZnCl2 and ZnSO4 present in bottom ash powder could react with moisture in bio-residue, forming hydrates of inorganic salt and breaking the polyacrylamide colloid, and lead to the release of adsorbed water in the bio-residue.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai.

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.