Comparative study and multi-parameter analysis to optimize device structure of triboelectric nanogenerators.

Triboelectric nanogenerator is becoming one of the most efficient energy harvesting device among all mechanical energy harvesters. This device consists of dielectric friction layers and metal electrode which generates electrical charges using electrostatic induction effect. There are several factors influencing the performance of this generator which needs to be evaluated prior to experiment. The absence of a universal technique for TENG simulation makes the device design and optimization hard before practical fabrication, which also lengthens the exploration and advancement cycle and hinders the arrival of practical applications. In order to deepen the understanding the core physic behind the working process of this device, this work will provide comparative analysis on different modes of TENG. Systematic investigation on different material combination, effect of material thickness, dielectric constant and impact of surface patterning is evaluated to shortlist the best material combination. COMSOL Multiphysics simulating environment is used to design, model and analyze factor affecting the overall output performance of TENG. The stationary study in this simulator is performed using 2D geometry structure with higher mesh density. During this study short circuit and open circuit condition were applied to observe the behavior of charge and electric potential produced. This observation is analyzed by plotting charge transfer/electric potential against various displacement distances of dielectric friction layers. The ouput is then provided to load ciruitary to measure the maximum output power of the models. Overall, this study provides an excellent understanding and multi-parameter analysis on basic theoretical and simulation modeling of TENG device.

Evaluating fundamental biochar properties in relation to water holding capacity.

Biochar products that hold and release water within a stable carbonised porous structure provide many opportunities for climate mitigation and a range of applications such as for soil amendments. Biochar that are produced from various organic feedstocks by pyrolysis can provide multiple co-benefits to soil including improving soil health and productivity, pH buffering, contaminant control, nutrient storage, and release, however, there are also risks for biochar application in soils. This study evaluated fundamental biochar properties that influence Water Holding Capacity (WHC) of biochar products and provides recommendations for testing and optimising biochar products prior to soil applications. A total of 21 biochar samples (locally sourced, commercially available, and standard biochars) were characterised for particle properties, salinity, pH and ash content, porosity, and surface area (with N2 as adsorbate), surface SEM imaging, and several water testing methods. Biochar products with mixed particle size, irregular shapes, and hydrophilic properties were able to rapidly store relatively large volumes of water (up to 400% wt.). In contrast, relatively less water (as low as 78% wt.) was taken up by small-sized biochar products with smooth surfaces, along with hydrophobic biochars that were identified by the water drop penetration test (rather than contact angle test). Water was stored mostly in interpore spaces (between biochar particles) although intra-pore spaces (meso-pore and micropore scale) were also significant for some biochars. The type of organic feedstock did not appear to directly affect water holding, although further work is needed to evaluate mesopore scale processes and pyrolytic conditions that could influence the biochemical and hydrological behaviour of biochar. Biochars with high salinity, and carbon structures that are not alkaline pose potential risks when used as soil amendments.

Advanced triboelectric nanogenerator-driven drug delivery systems for targeted therapies.

In the current decade, remarkable efforts have been made to develop a self-regulated, on-demand and controlled release drug delivery system driven by triboelectric nanogenerators (TENGs). TENGs have great potential to convert biomechanical energy into electricity and are suitable candidates for self-powered drug delivery systems (DDSs) with exciting features such as small size, easy fabrication, biocompatible, high power output and economical. This review exclusively explains the development and implementation process of TENG-mediated, self-regulated, on-demand and targeted DDSs. It also highlights the recently used TENG-driven DDSs for cancer therapy, infected wounds healing, tissue regeneration and many other chronic disorders. Moreover, it summarises the crucial challenges that are needed to be addressed for their universal applications. Finally, a roadmap to advance the TENG-based drug delivery system developments is depicted for the targeted therapies and personalised healthcare.

Optimising water holding capacity and hydrophobicity of biochar for soil amendment - A review.

Biochar is a product of the thermal treatment of biomass, and it can be used for enhancing soil health and productivity, soil carbon sequestration, absorbance of pollutants from water and soil, and promoting environmental sustainability. Extensive research has been done on applications of biochar to enhance the Water Holding Capacity (WHC) of biochar amended soil. However, a comprehensive road map of biochar optimised for enhanced WHC, and reduced hydrophobicity is not yet published. This review is the first to provide not only quantitative information on the impacts of biochar properties in WHC and hydrophobicity, but also a road map to optimise biochar for enhanced WHC when applied as a soil amendment. The review shows that straw or grass-derived biochar (at 500-600 °C) increases the WHC of soil if applied at 1 to 3 % in the soil. It is clear from the review that soil of varying texture requires different particle sizes of biochar to enhance the WHC and reduce hydrophobicity. Furthermore, the review concludes that ageing biochar for at least a year with enhanced oxidation is recommended for improving the WHC and reducing hydrophobicity compared to using biochar immediately after production. Additionally, while producing biochar a residence time of 1 to 2 h is recommended to reduce the biochar's hydrophobicity. Finally, a road map for optimising biochar is presented as a schematic that can be a resource for making decisions during biochar production for soil amendment.

A Smart Camera Trap for Detection of Endotherms and Ectotherms.

Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

Advancements in Algorithms and Neuromorphic Hardware for Spiking Neural Networks.

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in various application domains, including autonomous driving and drone vision. Researchers have been improving the performance efficiency and computational requirement of ANNs inspired by the mechanisms of the biological brain. Spiking neural networks (SNNs) provide a power-efficient and brain-inspired computing paradigm for machine learning applications. However, evaluating large-scale SNNs on classical von Neumann architectures (central processing units/graphics processing units) demands a high amount of power and time. Therefore, hardware designers have developed neuromorphic platforms to execute SNNs in and approach that combines fast processing and low power consumption. Recently, field-programmable gate arrays (FPGAs) have been considered promising candidates for implementing neuromorphic solutions due to their varied advantages, such as higher flexibility, shorter design, and excellent stability. This review aims to describe recent advances in SNNs and the neuromorphic hardware platforms (digital, analog, hybrid, and FPGA based) suitable for their implementation. We present that biological background of SNN learning, such as neuron models and information encoding techniques, followed by a categorization of SNN training. In addition, we describe state-of-the-art SNN simulators. Furthermore, we review and present FPGA-based hardware implementation of SNNs. Finally, we discuss some future directions for research in this field.

Design of a Highly Efficient Wideband Multi-Frequency Ambient RF Energy Harvester.

For low input radio frequency (RF) power from -35 to 5 dBm, a novel quad-band RF energy harvester (RFEH) with an improved impedance matching network (IMN) is proposed to overcome the poor conversion efficiency and limited RF power range of the ambient environment. In this research, an RF spectral survey was performed in the semi-urban region of Malaysia, and using these results, a multi-frequency highly sensitive RF energy harvester was designed to harvest energy from available frequency bands within the 0.8 GHz to 2.6 GHz frequency range. Firstly, a new IMN is implemented to improve the rectifying circuit's efficiency in ambient conditions. Secondly, a self-complementary log-periodic higher bandwidth antenna is proposed. Finally, the design and manufacture of the proposed RF harvester's prototype are carried out and tested to realize its output in the desired frequency bands. For an accumulative -15 dBm input RF power that is uniformly universal across the four radio frequency bands, the harvester's calculated dc rectification efficiency is about 35 percent and reaches 52 percent at -20 dBm. Measurement in an ambient RF setting shows that the proposed harvester is able to harvest dc energy at -20 dBm up to 0.678 V.

Disruptive technologies as a solution for disaster risk management: A review.

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Quad-Band Rectenna for Ambient Radio Frequency (RF) Energy Harvesting.

RF power is broadly available in both urban and semi-urban areas and thus exhibits as a promising candidate for ambient energy scavenging sources. In this research, a high-efficiency quad-band rectenna is designed for ambient RF wireless energy scavenging over the frequency range from 0.8 to 2.5 GHz. Firstly, the detailed characteristics (i.e., available frequency bands and associated power density levels) of the ambient RF power are studied and analyzed. The data (i.e., RF survey results) are then applied to aid the design of a new quad-band RF harvester. A newly designed impedance matching network (IMN) with an additional L-network in a third-branch of dual-port rectifier circuit is familiarized to increase the performance and RF-to-DC conversion efficiency of the harvester with comparatively very low input RF power density levels. A dual-polarized multi-frequency bow-tie antenna is designed, which has a wide bandwidth (BW) and is miniature in size. The dual cross planer structure internal triangular shape and co-axial feeding are used to decrease the size and enhance the antenna performance. Consequently, the suggested RF harvester is designed to cover all available frequency bands, including part of most mobile phone and wireless local area network (WLAN) bands in Malaysia, while the optimum resistance value for maximum dc rectification efficiency (up to 48%) is from 1 to 10 kΩ. The measurement result in the ambient environment (i.e., both indoor and outdoor) depicts that the new harvester is able to harvest dc voltage of 124.3 and 191.0 mV, respectively, which can be used for low power sensors and wireless applications.

Cloud- and Fog-Integrated Smart Grid Model for Efficient Resource Utilisation.

The smart grid (SG) is a contemporary electrical network that enhances the network's performance, reliability, stability, and energy efficiency. The integration of cloud and fog computing with SG can increase its efficiency. The combination of SG with cloud computing enhances resource allocation. To minimise the burden on the Cloud and optimise resource allocation, the concept of fog computing integration with cloud computing is presented. Fog has three essential functionalities: location awareness, low latency, and mobility. We offer a cloud and fog-based architecture for information management in this study. By allocating virtual machines using a load-balancing mechanism, fog computing makes the system more efficient (VMs). We proposed a novel approach based on binary particle swarm optimisation with inertia weight adjusted using simulated annealing. The technique is named BPSOSA. Inertia weight is an important factor in BPSOSA which adjusts the size of the search space for finding the optimal solution. The BPSOSA technique is compared against the round robin, odds algorithm, and ant colony optimisation. In terms of response time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 53.99 ms, 82.08 ms, and 81.58 ms, respectively. In terms of processing time, BPSOSA outperforms round robin, odds algorithm, and ant colony optimisation by 52.94 ms, 81.20 ms, and 80.56 ms, respectively. Compared to BPSOSA, ant colony optimisation has slightly better cost efficiency, however, the difference is insignificant.

Design and Numerical Analysis of a Graphene-Coated SPR Biosensor for Rapid Detection of the Novel Coronavirus.

In this paper, a highly sensitive graphene-based multiple-layer (BK7/Au/PtSe2/Graphene) coated surface plasmon resonance (SPR) biosensor is proposed for the rapid detection of the novel Coronavirus (COVID-19). The proposed sensor was modeled on the basis of the total internal reflection (TIR) technique for real-time detection of ligand-analyte immobilization in the sensing region. The refractive index (RI) of the sensing region is changed due to the interaction of different concentrations of the ligand-analyte, thus impacting surface plasmon polaritons (SPPs) excitation of the multi-layer sensor interface. The performance of the proposed sensor was numerically investigated by using the transfer matrix method (TMM) and the finite-difference time-domain (FDTD) method. The proposed SPR biosensor provides fast and accurate early-stage diagnosis of the COVID-19 virus, which is crucial in limiting the spread of the pandemic. In addition, the performance of the proposed sensor was investigated numerically with different ligand-analytes: (i) the monoclonal antibodies (mAbs) as ligand and the COVID-19 virus spike receptor-binding domain (RBD) as analyte, (ii) the virus spike RBD as ligand and the virus anti-spike protein (IgM, IgG) as analyte and (iii) the specific probe as ligand and the COVID-19 virus single-standard ribonucleic acid (RNA) as analyte. After the investigation, the sensitivity of the proposed sensor was found to provide 183.33°/refractive index unit (RIU) in SPR angle (θSPR) and 833.33THz/RIU in SPR frequency (SPRF) for detection of the COVID-19 virus spike RBD; the sensitivity obtained 153.85°/RIU in SPR angle and 726.50THz/RIU in SPRF for detection of the anti-spike protein, and finally, the sensitivity obtained 140.35°/RIU in SPR angle and 500THz/RIU in SPRF for detection of viral RNA. It was observed that whole virus spike RBD detection sensitivity is higher than that of the other two detection processes. Highly sensitive two-dimensional (2D) materials were used to achieve significant enhancement in the Goos-Hänchen (GH) shift detection sensitivity and plasmonic properties of the conventional SPR sensor. The proposed sensor successfully senses the COVID-19 virus and offers additional (1 + 0.55) × L times sensitivity owing to the added graphene layers. Besides, the performance of the proposed sensor was analyzed based on detection accuracy (DA), the figure of merit (FOM), signal-noise ratio (SNR), and quality factor (QF). Based on its performance analysis, it is expected that the proposed sensor may reduce lengthy procedures, false positive results, and clinical costs, compared to traditional sensors. The performance of the proposed sensor model was checked using the TMM algorithm and validated by the FDTD technique.

Cluster Analysis and Model Comparison Using Smart Meter Data.

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias.

The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

Electrothermal Modeling and Analysis of Polypyrrole-Coated Wearable E-Textiles.

The inhomogeneity of the resistance of conducting polypyrrole-coated nylon-Lycra and polyester (PET) fabrics and its effects on surface temperature were investigated through a systematic experimental and numerical work including the optimization of coating conditions to determine the lowest resistivity conductive fabrics and establish a correlation between the fabrication conditions and the efficiency and uniformity of Joule heating in conductive textiles. For this purpose, the effects of plasma pre-treatment and molar concentration analysis of the dopant anthraquinone sulfonic acid (AQSA), oxidant ferric chloride, and monomer pyrrole was carried out to establish the conditions to determine the sample with the lowest electrical resistance for generating heat and model the experiments using the finite element modeling (FEM). Both PET and nylon-Lycra underwent atmospheric plasma treatment to functionalize the fabric surface to improve the binding of the polymer and obtain coatings with reduced resistance. Both fabrics were compared in terms of average electrical resistance for both plasma treated and untreated samples. The plasma treatment induced deep black coatings with lower resistance. Then, heat-generating experiments were conducted on the polypyrrole (PPy) coated fabrics with the lowest resistance using a variable power supply to study the distribution and maximum value of the temperature. The joule heating model was developed to predict the heating of the conductive fabrics via finite element analysis. The model was based on the measured electrical resistance at different zones of the coated fabrics. It was shown that, when the fabric was backed with neoprene insulation, it would heat up quicker and more evenly. The average electrical resistance of the PPy-PET sample used was 190 Ω, and a maximum temperature reading of 43 °C was recorded. The model results exhibited good agreement with thermal camera data.

Advances in 4D-printed physiological monitoring sensors.

Physiological monitoring sensors have been critical in diagnosing and improving the healthcare industry over the past 30 years, despite various limitations regarding providing differences in signal outputs in response to the changes in the user's body. Four-dimensional (4D) printing has been established in less than a decade; therefore, it currently offers limited resources and knowledge. Still, the technique paves the way for novel platforms in today's ever-growing technologies. This innovative paradigm of 4D printing physiological monitoring sensors aspires to provide real-time and continuous diagnoses. In this perspective, we cover the advancements currently available in the 4D printing industry that has arisen in the last septennium, focusing on the overview of 4D printing, its history, and both wearable and implantable physiological sensing solutions. Finally, we explore the current challenges faced in this field, translational research, and its future prospects. All of these aims highlight key areas of attention that can be applied by future researchers to fully transform 4D printed physiological monitoring sensors into more viable medical products.

A Novel Ultra-Low Loss Rectangle-Based Porous-Core PCF for Efficient THz Waveguidance: Design and Numerical Analysis.

A novel, rectangle-based, porous-core photonic crystal fiber (PCF) has been modeled for the efficient propagation of a THz wave. The performance of the anticipated model has been assessed using the finite element method (FEM) in the range of 0.5-1.5 THz. Both the fiber core and cladding are modeled with rectangular air holes. Numerical analysis for this model reveals that the model has a lower amount of dispersion of about 0.3251 ps/THz/cm at 1.3 THz. Compared to the other THz waveguides, the model offers an ultra-lower effective material loss of 0.0039 cm-1 at the same frequency. The confinement loss is also lower for this model. Moreover, this model has a high-power fraction of about 64.90% at the core in the x-polarization mode. However, the effective area, birefringence, and numerical aperture have also been evaluated for this model. Maintenance of standard values for all the optical parameters suggests that the proposed PCF can efficiently be applied in multichannel communication and several domains of the THz technology.

A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network.

Pneumonia is a virulent disease that causes the death of millions of people around the world. Every year it kills more children than malaria, AIDS, and measles combined and it accounts for approximately one in five child-deaths worldwide. The invention of antibiotics and vaccines in the past century has notably increased the survival rate of Pneumonia patients. Currently, the primary challenge is to detect the disease at an early stage and determine its type to initiate the appropriate treatment. Usually, a trained physician or a radiologist undertakes the task of diagnosing Pneumonia by examining the patient's chest X-ray. However, the number of such trained individuals is nominal when compared to the 450 million people who get affected by Pneumonia every year. Fortunately, this challenge can be met by introducing modern computers and improved Machine Learning techniques in Pneumonia diagnosis. Researchers have been trying to develop a method to automatically detect Pneumonia using machines by analyzing and the symptoms of the disease and chest radiographic images of the patients for the past two decades. However, with the development of cogent Deep Learning algorithms, the formation of such an automatic system is very much within the realms of possibility. In this paper, a novel diagnostic method has been proposed while using Image Processing and Deep Learning techniques that are based on chest X-ray images to detect Pneumonia. The method has been tested on a widely used chest radiography dataset, and the obtained results indicate that the model is very much potent to be employed in an automatic Pneumonia diagnosis scheme.

A global life cycle assessment of manganese mining processes based on EcoInvent database.

This paper presents the life cycle assessment (LCA) carried out on the manganese beneficiation and refining process. This cradle-to-gate analysis is carried out using SimaPro software version 8.5. The considered case is the manganese beneficiation and refining process, and the final product is 1 kg of refined manganese. The global average dataset is collected from the EcoInvent and AusLCI database, which are originated from literature source. The analysis methodologies considered in this study are the International Life Cycle Reference Data System (ILCD) method and Cumulative Energy Demand (CED) method. A comparative analysis is also presented which compared among ILCD, Australian Indicator, and Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI) methods to identify the best practice method for global analysis of mining processes. A detailed sensitivity analysis has been carried out considering different scenarios, to suggest possible solutions to reduce the environmental impacts associated with manganese beneficiation and refining processes. The analysis results reveal that particulate matter, climate change, categories of eutrophication, human toxicity (cancer and non-cancer effects), and acidification are some of the noteworthy impact categories. The analysis results also showed that coal consumption is significantly higher than other types of renewables and non-renewable energy consumption in manganese beneficiation and refining processes. The analysis results further reveal that using the chromium steel in manganese beneficiation process and ferromanganese consumption in the refining process has a significant effect over other materials involved in manganese beneficiation and refining operations. The obvious reason behind this result is ferromanganese utilization as an energy-intensive process, which in turn increases the environmental emissions. The analysis results also showed that, between the beneficiation and refining process, manganese refining has a much greater impact on the environment rather than the beneficiation process due to the fossil fuel and electricity consumption in refining operations.

Impacts of aluminum production: A cradle to gate investigation using life-cycle assessment.

In this study, a life-cycle assessment from the aluminum production processes is carried out by considering four major steps of aluminum production - bauxite mining from the ore, alumina production, smelting, and the ingot casting process of aluminum. The study considered the cradle to gate system to analyze and quantify the environmental impacts during aluminum production process from raw materials to the final finished product. The production input and output datasets are taken from the databases and eGrid reports and the analysis is carried out utilizing three different methods, namely the International Life-cycle Reference Data System (ILCD) method, the Tool for Reduction and Assessment of Chemicals and Other Environmental Impacts (TRACI) method, and the Cumulative Energy Demand (CED) method. The results show electricity consumption in aluminum smelting causes noteworthy environmental burdens among all the processes or materials involved. Residual fuel oil, diesel, and natural gas consumption during the alumina production is also a major contributor to environmental impacts. Sensitivity analysis is conducted to justify the variation of environmental impacts in proportion with the modes of electricity generation from different sources to be used for aluminum smelting. Further sensitivity analysis is carried out among the quantity of the fossil fuel used based on their sources for process heat generation during alumina production. According to the sensitivity analysis results, renewable energy integration or reducing the consumption of fossil fuels would be the promising alternative to reduce the environmental burdens associated with aluminum production.

Life cycle analysis of copper-gold-lead-silver-zinc beneficiation process.

Gold, silver, lead, zinc, and copper are valuable non-ferrous metals that paved the way for modern civilisation. However, the environmental impacts from their beneficiation stage was always overlooked. This paper analysed the life cycle environmental impacts from the beneficiation process of gold-silver-lead-zinc-copper combined production. The analysis is conducted by utilising the SimaPro software version 8.5. The life cycle assessment methodologies followed are the International Reference Life Cycle Data System (ILCD) method, the IMPACT 2002+ method, and the Cumulative Energy Demand Method (CED). The most significant impact categories are ecotoxicity, climate change, human toxicity, eutrophication, acidification, and ozone depletion among nearly 15 impact categories which are assessed in this study. The analysis results from the ILCD method indicate that there is a noteworthy impact on ionising radiation caused by the beneficiation process. Out of the five metals considered, gold and silver beneficiation impacts the most while lead­zinc beneficiation impacts the least. Gold beneficiation has most impacts on the category of climate change and ecosystems. Other major impact categories are ionising radiation, terrestrial eutrophication, photochemical ozone formation, human toxicity, and acidification. The IMPACT 2002+ method shows the overall impact is on ecosystem quality and human health from this combined beneficiation process, dominantly from gold­silver beneficiation. The life-cycle inventory results show that the blasting process and the amount of electricity consumption in the beneficiation process contribute to cause significant amount of environmental impacts. The comparative impact results are presented and discussed in detail in this paper. Sensitivity analyses are presented based on various electricity grid-mix scenarios and energy-mix scenarios, and the results suggest that electricity grid mix has a dominant effect over the fossil-fuel mix. This paper also highlights the potential steps which could cut down the environmental effects by integrating renewable-energy technologies.