Waste Emissions Research Articles

Impact of Hydrogen on Emission Reduction in a CI Engine when Fuelled with Blends of Diesel-Waste Cooking Biodiesel

Waste utilisation and emission of greenhouse gases are two of the world's most pressing challenges today. This study seeks to solve the problem generated by the disposal of Waste Cooking Oil (WCO) as a tiny step forward in contributing to the reduction of waste disposal. WCO has a high potential as a bio fuel for internal combustion engines, hence it was chosen for this investigation. WCO of 20% and diesel 80% is used. This study looks at the performance, combustion and emission parameters of a dual fuel compression ignition engine running on WCO biodiesel with a hydrogen additive at atmospheric condition. The parametric study include variation in engine load (0-18 kg) and hydrogen flow rate (3, 6, 9 LPM). The experiment was conducted on a four-stroke diesel (computerized), single cylinder, water cooled power at 5.2 kW at 1500rpm Kirloskar engine. From the experiment overall parametric variation of 35.38%, 34.06% and 32.37% of brake thermal efficiency was obtained and emission was observed. However, with increase of hydrogen flow rate resulted in increased engine performance, increase in CO, NOx and reduction in emission of UBHC and CO2.

Exergy and energy analysis of Organic Rankine Cycle integration in the carbon black industry using pinch technology

Industrial Carbon black production is a petrochemical process that has high level of waste heat and emissions. Main points of CB process include furnace and stack lines. Also, the use of different feedstocks can be effective in level of waste heat and amount of emission pollutants. So the main objective of this research use from the pinch technology in CB process in order to minimize the level of waste heat by utilization of waste heat recovery (WHR) applications for power generation. By numerical investigation and modelling of WHR in furnace and stack points, can be defined best location for power generation in CB process. Moreover, through the analysis of Organic Rankine Cycle (ORC) systems integrated with WHR from both energy and exergy perspectives, second target to enhance their thermal efficiency. The results indicate that the presence of blowers on stacks leads to an increase in RoHR and RoHC. In order to determine the optimal location for power generation cycle installation, an ORC energy and exergy analysis was conducted. The results showed that the energy and exergy values at the SLC stack were higher than those at the HLC point. Also, numerical results showed the points that have suitable thermal efficiency and rate of exergy of WHR where have higher temperature gradient than other points. So SLC (ĖxWHR=166.7kW) and HLC (ĖxWHR=136.1kW) stacks integrated with blower are the optimal location for WHR utilization in the CB production process.

Climate change mitigation through strengthening of waste bank role and 3R (reduce, reuse, recycle) application in urban area

Waste problems and climate change that are not properly anticipated can result in flooding, a dirty environment, and poor public health. A good life will emerge when there is harmony with the environment. Still, some people have not realized the importance of harmony with nature and the environment in urban communities. Big cities in Indonesia are the biggest waste contributors. Various efforts have been made, such as optimizing the role of waste banks and strengthening the application of the 3R model (Reduce, Reuse, Recycle). Therefore, this study aims to examine the role of waste banks and 3R implementation in climate change. This study uses a descriptive method with in-depth interviews with waste bank managers and some people in Surakarta. The result shows that waste banks and 3R applications can be an alternative to addressing waste problems and mitigating climate change. Waste banks and implementing 3R play an important role in reducing waste and carbon gas emissions. As a result, it can reduce temperature extremes. Good waste management will also improve environmental quality and reduce the risk of flooding due to the smooth water flow without being clogged with garbage.

Correlation between chemical composition, wettability and carbon anode performance: Exploration of high quality carbon anode preparation for aluminum electrolysis

The chemical composition of coal tar pitch has a significant effect on its wettability and the performance of carbon anode for aluminum electrolysis. This study aimed to reveal the correlation among chemical composition, wettability, and carbon anode performance, as well as provide strategies for reducing energy consumption, greenhouse gases, and hazardous waste emissions during aluminum electrolysis processes. First, the chemical composition of modified coal tar pitch was regulated by adding low-temperature coal tar pitch. The effects of low-temperature coal tar pitch mixing amount on the chemical composition, basic properties, contact angle, static wettability, functional groups, and coke microstructure were studied. In particular, electron paramagnetic resonance (EPR) and atomic force microscopy (AFM) were performed to qualitatively and quantitatively analyze radical emissions and surface structure of coal tar pitch at the microscopic scale. Subsequently, the correlation of chemical composition, wettability, and performance was discussed according to the effect of low-temperature coal tar pitch on the performance of carbon anode. Results showed that the incorporation of low-temperature coal tar pitch reduced the softening point and viscosity, and the increase in fluidity improved the contact angle and static wettability. Meanwhile, the increase in volatile content and the decrease in coking value, surface roughness, nanoscale adhesion, and free radical release concentration jointly weakened the bonding strength between coal tar pitch and calcined petroleum coke. The performance of the carbon anode prepared by blending low-temperature coal tar pitch weakened, except for the ash content. Consequently, the balance between the wettability and bonding strength of coal tar pitch is essential for the preparation of high-quality anode for aluminum electrolysis.

Industrially relevant pyrolysis of diverse contaminated organic wastes: Gas compositions and emissions to air

Pyrolysis is a promising waste handling technique compared to incineration, especially due to its potential for greenhouse gas reduction through biochar carbon removal. This study investigated greenhouse gas and air pollutant emissions and emission factors from waste feedstocks and a reference clean wood pyrolyzed at 500–800 °C in an industrially relevant small version Biogreen® unit with condensation prior to pyrolysis gas combustion. Emissions were generally lower than literature values, except for nitrogen oxides (NOX) and sulfur dioxide (SO2). Methane (CH4) and nitrous oxide (N2O) emissions ranged 30–570 mg CH4 and 0–32 mg N2O per kg biochar produced. Waste lignocellulosics (waste timber and garden waste) had comparable emissions to clean wood, except for higher NOX emissions. All waste feedstocks exceeded the EU NOX emission limit value for waste incineration (>200 mg NOX/Nm3 at 11% oxygen, O2), while no carbon monoxide (CO) was measured (<0.6 mg/Nm3) suggesting possible compliance with EU emission limit values for waste incineration with simple pollution control measures such as air-to-fuel ratio regulation, or other NOX reducing measures such as flue gas recirculation or selective (non-)catalytic reduction. Sludges and food waste reject also exceeded the SO2 EU emission limit value (50 mg SO2/Nm3 at 11% O2) for waste incineration, emissions ranging 61–298 mg SO2/Nm3 at 11% O2, indicating the potential need for SO2 pollution control. In conclusion, this study shows continuous pyrolysis with condensation as a promising alternative for waste management with potential for simplified air pollution control compared to incineration. Future work should focus on optimized combustion systems for waste pyrolysis and emissions from waste pyrolysis without condensation.

Charting our sustainability journey within the Division of Surgery and Interventional Science at University College London

AbstractThis article describes a case study of our journey to running more sustainable labs within the Division of Surgery and Interventional Science at University College London (UCL), London, United Kingdom. Through the setting up of a self-assessment team within the division, we asked the key question, ‘what does sustainability mean in science and how will we apply this to our academic Division?’ Our division’s sustainability team took on the challenges to tackle unsustainable practise, primarily within our laboratories. By considering and implementing simple steps within our research department, we have reduced lab waste as well as decreased our overall carbon emission. We have clarified our hallmarks of sustainability and seek to share our changed practices to provide clear and easy guidance for how to make medical research divisions sustainable based on the actions taken in our labs. This study provides guideline on how to make academic research more sustainable by describing simple steps to implement in the laboratories. These steps were described using Division of Surgery and Interventional Science at UCL as a case study. The division’s sustainability team develops sustainable lab practices, which has led to reduction in lab waste and carbon emissions.

Establishing an emission inventory for ammonia, a key driver of haze formation in the southern North China plain during the COVID-19 pandemic

Despite the significant reduction in atmospheric pollutant levels during the COVID-19 lockdown, the presence of haze in the North China Plain remained a frequent occurrence owing to the enhanced formation of secondary inorganic aerosols under ammonia-rich conditions. Quantifying the increase or decrease in atmospheric ammonia (NH3) emissions is a key step in exploring the causes of the COVID-19 haze. Historic activity levels of anthropogenic NH3 emissions were collected through various yearbooks and studies, an anthropogenic NH3 emission inventory for Henan Province for 2020 was established, and the variations in NH3 emissions from different sources between COVID-19 and non-COVID-19 years were investigated. The validity of the NH3 emission inventory was further evaluated through comparison with previous studies and uncertainty analysis from Monte Carlo simulations. Results showed that the total NH3 emissions gradually increased from north-west to south-east, totalling 751.80 kt in 2020. Compared to the non-COVID-19 year of 2019, the total NH3 emissions were reduced by approximately 4 %, with traffic sources, waste disposal and biomass burning serving as the sources with the top three largest reductions, approximately 33 %, 9.97 % and 6.19 %, respectively. Emissions from humans and fuel combustion slightly increased. Meanwhile, livestock waste emissions decreased by only 3.72 %, and other agricultural emissions experienced insignificant change. Non-agricultural sources were more severely influenced by the COVID-19 lockdown than agricultural sources; nevertheless, agricultural activities contributed 84.35 % of the total NH3 emissions in 2020. These results show that haze treatment should be focused on reducing NH3, particularly controlling agricultural NH3 emissions.

Treatment of salt from hides curing stage by electrocoagulation for use in the pickling stage of the tanning industry

The raw materials for the tanning industry, namely hides and skins, are preserved (curing stage) and carried with common salt, i.e., sodium chloride (NaCl). Proceeding to conversion into leather, pickling is a key stage of the tannery process, which entails high demand of water and salt. In this work, the salt-derived brine (SdB) generated from the curing of hides was treated by iron-driven electrocoagulation (EC), aiming at its later application in the pickling stage of the tanning industry, promoting a transition to zero waste emission policy. Focusing on reducing the brine's total organic carbon (TOC), central composite rotational design and response surface methodology were adopted to study the effect of electrolysis time (6.2–14.2 min) and current density (74–431 A·m−2) on the treatment of the SdB (≅ 7.5 % wt. NaCl). The quality of the treated brines was then assessed in pickling trials and compared with virgin brine. 68–83 % removal of TOC from the SdB were achieved under electrolysis time ranging 6.2–14.2 min and current density ranging 126–252 A·m−2. Under these operating ranges the quality of the wet-blue leathers was guaranteed. Lowest power consumption (0.44 kWh·m−3) was achieved under electrolysis time of 6 min and current density of 126 A·m−2, yielding 68 % removal of TOC. Moreover, the shrinkage temperature of the hides was improved with treated brine (103.5 °C–110.5 °C) compared to virgin brine (103.0 °C). The present study provides strong evidence that contaminated salt from the curing stage can be valorised within the tanning industry through electrocoagulation treatment and then used in another production stage, instead of being landfilled.

Data-driven low-carbon transformation management for manufacturing enterprises: an eco-efficiency perspective.

The low-carbon transformation of manufacturing enterprises is considered to be imperative to achieve carbon neutrality. Therefore, we propose a data-driven strategy to achieve a low-carbon transformation of manufacturing enterprises from an eco-efficiency perspective. Following the collection of input (energy, materials, equipment, R&D, and services) and output (waste and products) data from production systems of manufacturing enterprises, an ecological efficiency model of manufacturing enterprise production system was constructed from the perspective of carbon emissions, thus allowing the quantitative evaluation of the ecological efficiency of the production system. Furthermore, a "measurable, evaluable, and optimized" low-carbon transformation and upgrading method for manufacturing enterprise production system was established. Finally, through the production practice data of an enterprise from 2017 to 2021, the feasibility and effectiveness of this method were verified. The results show that this method can effectively improve the ecological efficiency of enterprises by 3.6% and reduce waste emissions by 12%. Our study provides new tools for improving the ecological efficiency of manufacturing systems, along with theoretical and methodological support to manufacturing enterprises for low-carbon transformation.

Managing Water-Energy-Carbon Nexus for Urban Areas With Ambiguous Moment Information

The rapid growth of electric load fuels urbanization but creates unsettling environmental challenges. In that regard, the energy system is a crucial part of consuming water and emitting greenhouse gases. To support reliable and economical energy system operations, with reduced water waste and carbon emissions, we design a viable co-optimization of water-energy-carbon nexus for an integrated energy system by adequately modelling essential interdependencies of power, gas, and water systems. A two-stage distributionally robust optimization method is proposed: the first-stage model minimizes the day-ahead reserve capacity scheduling for the next day, and the second-stage model enables real-time dispatch by considering the variability in renewable power generation. The carbon emissions by power generation and distribution are incorporated in both stages to ensure low-carbon operations. We use a two-stage moment-based distributionally robust approach to capture and represent the uncertainties in renewable generation. Instead of assuming a deterministic distribution, we characterize an ambiguity set with mean vectors and covariance matrices, which produces a family set of distributions. We conduct case studies using a modified IEEE 33-bus power system that includes and links a 20-node gas system and a 10-node water system. The comparative results indicate that the proposed co-optimization of water-energy-carbon nexus is superior than several benchmarks. By considering the close interdependencies of water, energy, and carbon, we create an optimal resource allocation scheme, which is capable of minimizing operation costs, lessening carbon emissions, and reducing water waste. The current research enables effective operation schemes for urban energy systems, illustrates a viable way to curb local environmental emissions, and increases water usage efficiency in the water-energy-carbon nexus.

Online Coal Identification Based on One-Dimensional Convolution and Its Industrial Applications

In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations may lead to low combustion efficiency of boilers, cause disturbances to the normal operation of thermal power units, increased energy waste and carbon emissions, and even lead to serious explosion accidents. Therefore, the online identification of coal types for thermal power units is of great significance. To obtain the precise type of coal online, in the present work, a data-driven coal identification method is proposed based on convolutional networks that do not necessitate additional hardware detection equipment and are easy to implement. Experimental results indicate that the proposed method exhibits superior performance in comparison to traditional methods, thus ultimately improving the performance of thermal power plant.

Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion

Improper fuel loading decision results in carrying excessive dead weight during flight operation, which will burden the airline operation cost and cause extra waste emission. Existing works mainly focused on the post-event fuel consumption based on flight trajectory. In this work, a novel deep learning model, called FCPNet, is proposed to achieve the fuel consumption prediction (FCP) before the flight departure. Considering the influential factors for aircraft performance, the multi-modal information sources, including the planned route, weather information, and operation details, are selected as the model input to predict fuel consumption. Correspondingly, three modules are innovatively proposed to learn embedding features from multi-modal inputs. Based on the planned route, the graph convolutional network is proposed to mine the spatial correlations in the non-Eulerian route network. Considering the grid attributes of the weather information, the ConvLSTM is applied to learn abstract representations from both the temporal and spatial dimensions, in which the three-dimensional convolution neural networks are also designed to fine-tune intermediate feature maps. The fully connected layer is also proposed to learn informative features from operation details. Finally, an attention-based fusion network is presented to generate the final embedding by considering the unique contributions of the multi-modality sources, which are further applied to predict flight fuel consumption. A binary encoding representation is proposed to formulate the FCP task as a multi-binary classification problem. The proposed model is validated on a real-world dataset, and the results demonstrate that it outperforms other baselines, i.e., achieving a 6.50% mean absolute percentage error, which can practically support the airline operation and global emission control before flight departure.

Artificial Intelligence-Based Temperature Twinning and Pre-Control for Data Center Airflow Organization

Green and low-carbon has become the main theme of global energy development. Data centers are the core of the digital age, carrying huge arithmetic demand. Data centers must implement green low-carbon energy efficiency management to improve energy efficiency, reduce energy waste and carbon emissions, and achieve sustainable development. As a result, an intelligent management strategy for dynamic energy efficiency of data center networks with Artificial Intelligence (AI) fitting control is proposed. Firstly, a Long Short-Term Memory (LSTM) network is used for long sequence trend prediction to predict the temperature of the data center in the next sequence using the temperature of the past 15 sequences and the power consumption of the equipment as parameters. Then, based on the prediction results, the intelligent air conditioning controller based on Deep Q-Network (DQN) is designed to update the parameters by using the gradient of double-Q network and error backpropagation, and the optimal control action is selected by using the ε-greedy strategy to ensure that the prediction of the hotspot does not occur. Experiments show that the average absolute errors of temperature prediction for supply air, return air, cold aisle as well as hot aisle are 0.32 °C, 0.21 °C, 0.36 °C and 0.19 °C, respectively. The Power Usage Effectiveness (PUE) and Water Usage Effectiveness (WUE) decreased by an average of 2.6% and 2.5%, respectively. The method achieves the purpose of predicting future temperatures and intelligently controlling the output so that the data center can satisfy the premise of normal operation and thus achieve more efficient energy use.

Insight into the gas pollutants emission of rural solid waste during the gasification-combustion process: Influencing factors and mechanisms

With the rapid development of rural areas, effective disposal of rural solid waste (RSW) has emerged as a critical concern. In this study, the gasification-combustion technology concerning air-staged technology was employed in the treatment of RSW and its main components (rubber, PVC, and food waste(FW)). This study focused on analyzing the emission patterns and total amounts of NOx, SO2, and HCl. For RSW, the gasification-combustion experiment effectively reduced NOx and SO2 emissions compared to the combustion experiment. The amount and patterns of gaseous pollutant emissions were significantly impacted by changes in gasification temperature, combustion temperature, and proportion of air (PA). Besides, due to the interplay of RSW's constituents, the change in gasification temperature and PA resulted in different emission patterns for FW and RSW. Under the combined action of constituents, the trend of RSW SO2 emission amount was distinct from its S sources due to the potential sulfation impact. The RSW's HCl emissions were found to be minimally affected by changes in temperature, PA, and reaction technique. This research manages the miniaturized centralized incinerator disposal of RSW and helps the control of gas pollutants emission.

Greenhouse gas emission reduction potential, energy and exergy analysis of combined microwave-convective dryer

About 12–25% of the energy used in the food processing industries in the drying section and various technologies such as the combination of microwave and convective methods has been applied to reduce greenhouse gases and environmental pollution effects. In this study, the effects of microwave power (200–500 W) and drying air temperatures (30–55 °C) on energy and exergy aspects, GHG emissions (CO2, SO2, and NOx), and GHG reduction potential were evaluated and optimized using genetic algorithm approach in a combined microwave-convective drying process. The results showed that higher drying air temperature caused an increasing energy loss and decreasing exergy efficiency. The energy and exergy efficiencies were found to be in the range of 5.20–23.50% and 5.12–9.19%, respectively. The exergy improvement potential and GHG emissions decreased with increasing microwave power and decreasing drying air temperature. The total GHG emissions varied between 2002.4 and 7627.4 (g/kg water). Optimization of the drying parameters for the given constraints resulted in 498 W and 30.2 °C. The GHG emissions can be reduced 14.1%–28.6% with a recovery of exergy loss. Therefore, it can be concluded that heat recovery mechanisms are needed to reduce exergy waste and GHG emissions.

Perception Analysis of the Transformation from a Fishery-Led to Industry-Led Island with its Human Settlement Changes: A Case Study of Liuheng Island, Zhoushan City, China

Based on the theories of industrial structure and settlement environment, this paper conducts a quantitative and qualitative study to investigate how the evolution in the industrial structure of the island affects the settlement environment with relevant statistical data, local chronicles, and questionnaire survey data. The study found that the industrial structure of Liuheng Island has transformed from a fishery-led to industry-led island, which has led to the rapid increase of urbanization on Liuheng Island, and the public service facilities have been increasingly densely distributed urban-type communities with high modernization levels, such as Longshan Community, Xuanzhuotou Community, and Taimen Community. The local residents of Liuheng Island are also satisfied with the public service facilities in terms of facility availability, transportation convenience, environmental comfort, and environmental health. For a period, the island residents were particularly concerned about the regional differences in environmental health, and the main reasons were the industrial structure upgrade towards industrialization, which would cause certain industrial waste emissions, and the pressure of commuting within the island, which would affect residents’ sense of security. This indicates that the evolution of industrial structure will directly affect the speed and quality of urban and rural settlement hardware construction and also, to a certain extent, the subjective perceptions of residents around the dense industrial areas on the state of the settlement, especially in terms of environmental health and community living safety.

Evaluating the Environmental Impact of Single-Use and Multi-Use Surgical Staplers with Staple Line Buttressing in Laparoscopic Bariatric Surgery.

Operation rooms have a large environmental impact. Single-use staplers (SUS) are widely used surgical instruments that contribute to resource consumption and waste generation, whereas multi-use staplers (MUS) can greatly reduce the environmental impact of surgery. The staple lines are often reinforced with buttressing material to prevent leaks and bleeding. We explore current clinical practice and environmental concerns regarding stapling and buttressing, as well as the environmental impact of staple line buttressing in sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB). Furthermore, we extend this analysis by taking packaging material and the lithium in power supplies into consideration. A survey of bariatric surgeons was conducted to assess stapler and buttressing use in clinical practice. We deconstructed and analyzed the product and packaging composition of a commonly used SUS with separate staple line reinforcement (Echelon Flex™ with Echelon Endopath™, Ethicon) and MUS (Signia™ with Tri-Staple™ reinforced reloads, Medtronic), where the buttressing material was delivered separately or already incorporated in the reload cartridge, respectively. Both systems were compared regarding total waste generation, resource use (determined as total material requirement), and greenhouse gas emission caused by their lithium content. 60 mm cartridges were most frequently used in bariatric surgery, and 67% of surveyed surgeons applied staple line reinforcement. MUS with pre-attached buttressing resulted in a reduction of waste, material consumption, and greenhouse gas emissions compared to SUS with separate buttressing: they reduced product waste by 40% (SG and RYBG), packaging waste by 60% (SG) and 57% (RYGB), resource consumption by more than 90%, and greenhouse gas emissions related to the lithium in the batteries by 99.7%. Preloaded buttressing produced less waste than separate buttressing per stapler firing. The environmental impact of surgery can be greatly reduced by using MUS with pre-attached buttressing rather than SUS with separate buttressing.

Verifying Methane Inventories and Trends With Atmospheric Methane Data

AbstractThe 2015 Paris Climate Agreement and Global Methane Pledge formalized agreement for countries to report and reduce methane emissions to mitigate near‐term climate change. Emission inventories generated through surface activity measurements are reported annually or bi‐annually, and evaluated periodically through a “Global Stocktake.” Emissions inverted from atmospheric data support evaluation of reported inventories, but their systematic use is stifled by spatially variable biases from prior errors combined with limited sensitivity of observations to emissions (also called smoothing error), as‐well‐as poorly characterized information content. Here, we demonstrate a Bayesian, optimal estimation (OE) algorithm for evaluating a state‐of‐the‐art inventory (EDGAR v6.0) using satellite‐based emissions from 2009 to 2018. The OE algorithm quantifies the information content (uncertainty reduction, sectoral attribution, spatial resolution) of the satellite‐based emissions and disentangles the effect of smoothing error when comparing to an inventory. We find robust differences between satellite and EDGAR for total livestock, rice, and coal emissions: 14 ± 9, 12 ± 8, −11 ± 6 Tg CH4/yr respectively. EDGAR and satellite agree that livestock emissions are increasing (0.25–1.3 Tg CH4/yr/yr), primarily in the Indo‐Pakistan region, sub‐tropical Africa, and the Southern Brazilian; East Asia rice emissions are also increasing, highlighting the importance of agriculture on the atmospheric methane growth rate. In contrast, low information content for the waste and fossil emission trends confounds comparison between EDGAR and satellite; increased sampling and spatial resolution of satellite observations are therefore needed to evaluate reported changes to emissions in these sectors.

A Critical Review on Electric Vehicle Batteries — Based on Life Cycle Assessment Method

Currently, the production of electric vehicles and the lithium batteries that powers them is increasing significantly. Thus, this dissertation combines the literature review approach and the life cycle assessment (LCA) method to investigate the environmental or social impact of lithium batteries for electric vehicles from cradle to grave. According to the literature review, the environmental impacts of lithium batteries include carbon dioxide and harmful electrolyte emissions, water pollution, air pollution, solid waste emissions, gaseous pollutants, volatile organic compounds, nitrogen oxides, heavy metals, organic pollutants, particulate matter and CO2 emissions. Therefore, lithium batteries for electric vehicles pose a great environmental concern. In addition, the confusion surrounding the definition of lithium batteries in LCA can pose a significant concern. The in-depth analysis reveals that there is a lack of uniform data on EV lithium batteries, a low recycling rate of lithium batteries, and a lack of waste stream management. There is no uniformity in the technology and development of lithium batteries among countries such as China, Canada, and the USA. As a result, the secondary data may only represent this region. Besides, the literature consulted covers a long period of time. There is little possibility that the negative environmental impact of earlier technology like the burning of lithium batteries in incinerators can now be improved. Moreover, lithium batteries are still in the development stage. Therefore, there is still room for potential development in the future.

A novel combined metallurgy-beneficiation method for the facile and low-cost comprehensive resource utilization of low-grade kaolin solid wastes

Low-grade kaolin is the largest emissions of industrial solid waste that is difficult to dispose of and pollutes the environment seriously. From the perspective of harmless and complete resource utilization, we proposed a novel strategy that combines the wet leaching under mild conditions and physical beneficiation for the facile and low-cost high-valued utilization of low-grade kaolin that involves high-efficiency recovery of aluminum (Al), silicon (Si), and titanium (Ti). The key to successful implementation of this method lies in the new discovery that the residual SiO2 after Al extraction of kaolinite by acid leaching under specific conditions could be rapidly dissolved in dilute NaOH solution at room temperature 25 °C. This highly reactive SiO2 challenges the conventional notions of various silica species are usually chemically stable. By adjusting the key technical parameters of the thermal activation-acid leaching process, the selective and efficient extraction of Al2O3 from low-grade kaolin was realized. The acid leaching residue was then subjected to selective recovery of SiO2 by alkaline leaching at 25 °C to obtain high-quality sodium silicate. Finally, the alkali leaching residue as titanium coarse concentrate was separated by centrifugal concentrator to obtain artificial rutile (TiO2 >91.06%). The key mechanism for the formation of the highly reactive silica was also systematically studied and confirmed.