Enhanced removal of acetaminophen from synthetic wastewater using multi-walled carbon nanotubes (MWCNTs) chemically modified with NaOH, HNO3/H2SO4, ozone, and/or chitosan

peer-reviewedThis study investigates the technical feasibility of MWCNTs for acetaminophen (Ace) removal from synthetic wastewater in batch mode. To improve their removal performance, the surface of the MWCNTs was chemically modified with NaOH, HNO3/H2SO4, ozone and/or chitosan. The effects of pertinent parameters such as reaction time, dose, pH, and agitation speed on the Ace removal were evaluated. Their removal performance on Ace was compared to those of previous studies. The adsorption mechanisms of Ace removal by the MWCNTs are also presented. It is evident from this study that after chemical modification on its surface, the treated nano-adsorbent significantly enhanced Ace removal from wastewater. Among all types of those adsorbents, the ozone-treated MWCNT stands out for the highest Ace removal (95%) under the same initial Ace concentration of 10 mg/L. Their adsorption capacities, applicable to the Freundlich isotherm model, are listed as: ozone-treated MWCNT (250 mg/g) > chitosan-coated MWCNT (205 mg/g) > acid-treated MWCNT (160 mg/g) > NaOH-treated MWCNT (130 mg/g) > as-received MWCNT (90 mg/g). Although the ozone-treated MWCNT has the most outstanding performance in Ace removal, its treated effluent still could not meet the required effluent limit of less than 0.2 mg/L set by China’s legislation. This suggests that further treatment using biological processes needs to be carried out to complement Ace removal from the wastewater samples

Pseudo-solidification of dredged marine soils with cement - fly ash for reuse in coastal development

The dislodged and removed sediments from the seabed, termed dredged marine soils, are generally classified as a waste material requiring special disposal procedures. This is due to the potential contamination risks of transporting and disposing the dredged soils, and the fact that the material is of poor engineering quality, unsuitable for usage as a conventional good soil in construction. Also, taking into account the incurred costs and risk exposure in transferring the material to the dump site, whether on land or offshore, it is intuitive to examine the possibilities of reusing the dredged soils, especially in coastal development where the transportation route would be of shorter distance between the dredged site and the construction location. Pseudo-solidification of soils is not a novel idea though, where hydraulic binders are injected and mixed with soils to improve the inherent engineering properties for better load bearing capacity. It is commonly used on land in areas with vast and deep deposits of soft, weak soils. However, to implement the technique on the displaced then replaced dredged soil would require careful study, as the material is far more poorly than their land counterparts, and that the deployment of equipment and workforce in a coastal environment is understandably more challenging. The paper illustrates the laboratory investigation of the improved engineering performance of dredged marine soil sample with cement and fly ash blend. Some key findings include optimum dosage of cement and fly ash mix to produce up to 30 times of small strain stiffness improvement, pre-yield settlement reduction of the treated soil unaffected by prolonged curing period, and damage of the cementitious bonds formed by the rather small dosage of admixtures in the soil post-yield. In short, the test results show a promising reuse potential of the otherwise discarded dredged marine soils

Application of choosing by advantages to determine the optimal site for solar power plants

Solar energy is a critical component of the energy development strategy. The site selection for solar power plants has a signifcant impact on the cost of energy production. A favorable situation would result in signifcant cost savings and increased electricity generation efciency. California is located in the southwest region of the United States of America and is blessed with an abundance of sunlight. In recent years, the state’s economy and population have expanded quickly, resulting in an increased need for power. This study examines the south of California as a possibly well-suited site for the constructing large solar power plants to meet the local electricity needs. To begin, this article imposed some limits on the selection of three potential sites for constructing solar power plants (S1, S2, and S3). Then, a systematic approach for solar power plant site selection was presented, focusing on fve major factors (economic, technological, social, geographical, and environmental). This is the frst time that the choosing by advantages (CBA) method has been used to determine the optimal sites for solar power plant construction, with the possible sites ranked as S2>S1 >S3. The results were then compared with traditional methods such as the multi-criteria decision-making method. The fndings of this study suggest that the CBA method not only streamlines the solar power plant site selection process but also closely aligns with the objectives and desires of the investors

A hybrid SDS and WPT-IBBO-DNM based model for ultra-short term photovoltaic prediction

Accurate photovoltaic (PV) power prediction has been a subject of ongoing study in order to address grid stability concerns caused by PV output unpredictability and intermittency. This paper proposes an ultra-short-term hybrid photovoltaic power forecasting method based on a dendritic neural model (DNM) in this paper. This model is trained using improved biogeography-based optimization (IBBO), a technique that incor�porates a domestication operation to increase the performance of classical biogeography-based optimization (BBO). To be more precise, a similar day selection (SDS) technique is presented for selecting the training set, and wavelet packet transform (WPT) is used to divide the input data into many components. IBBO is then used to train DNM weights and thresholds for each component prediction. Finally, each component’s prediction results are stacked and reassembled. The suggested hybrid model is used to forecast PV power under various weather conditions using data from the Desert Knowledge Australia Solar Centre (DKASC) in Alice Springs. The simulation results indicate that the proposed hybrid SDS and WPT-IBBO-DNM model has the lowest error of any of the benchmark models and hence has the potential to considerably enhance the accuracy of solar power forecasting (PVPF)

Estimation of the state of health (SOH) of batteries using discrete curvature feature extraction

Lithium battery applications in a variety of engineering sectors must be safe and reliable while maintaining a high level of energy efficiency. An accurate assessment of the battery's state of health (SOH) is critical in battery management systems (BMS). In recent years, it has been proved that machine learning is effective at estimating SOH. This work proposes a novel approach of health indicator (HI) extraction based on the U-chord curvature model, based on a complete analysis of battery aging data. In contrast to previous approaches for feature extraction, our method splits the discharge process into various phases based on the curvature of the discharge curve and extracts many HIs with a high correlation to battery SOH in the discharge platform stage of the discharge curve. To demonstrate the superiority of the proposed model, several well-known machine learning algorithms are employed to estimate SOH using extracted attributes. Long short-term memory (LSTM) and artificial neural networks (ANNs) are examples of these techniques. Accuracy, reliability, and robustness of the proposed model are evaluated using three publicly available data sets. According to the data, the model appears to be capable of accurately calculating the battery's SOH, with a mean absolute error of less than 1.08% and a root mean square error of less than 1.46% for various battery types

2D Graphene oxide (GO) doped p-n type BiOI/Bi 2 WO 6 as a novel composite for photodegradation of bisphenol A (BPA) in aqueous solutions under UV-vis irradiation

Abstract(#br)Bisphenol A (BPA) is a refractory pollutant presents in water body that poses serious threats to living organisms. To deal with it, we investigate and evaluate the effectiveness of GO@BiOI/Bi 2 WO 6 composite as a novel photocatalyst for BPA removal from aqueous solutions under UV–vis irradiation. To enhance its removal for BPA, the surface of BiOI/Bi 2 WO 6 is modified with graphene oxide (GO). This composite is named as ‘GO@BiOI/Bi 2 WO 6 ’. Changes in its physico-chemical properties after surface modification with GO are characterized by XRD, FTIR, FESEM-EDS, XPS, PL, and BET methods. Optimized conditions of BPA degradation by the composite are determined under identical conditions. Photodegradation pathways of BPA and its removal mechanisms by the same composite are presented. It is obvious that the GO@BiOI/Bi 2 WO 6 has demonstrated its potential as a promising photocatalyst for BPA removal under UV–vis irradiation. About 81% of BPA removal is attained by the GO@BiOI/Bi 2 WO 6 under optimized conditions (10 mg/L of BPA, 0.5 g/L of dose, pH 7 and 5 h of reaction time). The oxidation by-products of BPA degradation include p -hydroquinone or 4-(1-hydroxy-1-methyl-ethyl)-phenol. In spite of its performance, the treated effluents are still unable to meet the maximum discharge limit of <1 mg/L set by national legislation. Therefore, subsequent biological processes are essential to maximize its biodegradation in the wastewater samples before their discharge into waterbody

Improving the Performance of DC Microgrids by Utilizing Adaptive Takagi-Sugeno Model Predictive Control

In naval direct current (DC) microgrids, pulsed power loads (PPLs) are becoming more prominent. A solar system, an energy storage system, and a pulse load coupled directly to the DC bus compose a DC microgrid in this study. For DC microgrids equipped with sonar, radar, and other sensors, pulse load research is crucial. Due to high pulse loads, there is a possibility of severe power pulsation and voltage loss. The original contribution of this paper is that we are able to address the nonlinear problem by applying the Takagi-Sugeno (TS) model formulation for naval DC microgrids. Additionally, we provide a nonlinear power observer for estimating major disturbances affecting DC microgrids. To demonstrate the TS-potential, we examine three approaches for mitigating their negative effects: instantaneous power control (IPC) control, model predictive control (MPC) formulation, and TS-MPC approach with compensated PPLs. The results reveal that the TS-MPC approach with adjusted PPLs effectively shares power and regulates bus voltage under a variety of load conditions, while greatly decreasing detrimental impacts of the pulse load. Additionally, the comparison confirmed the efficiency of this technique

Multi-convolution feature extraction and recurrent neural network dependent model for short-term load forecasting

Load forecasting is critical for power system operation and market planning.With the increased penetration of renewable energy and the massive consumption of electric energy, improving load forecasting accuracy has become a dif�cult task. Recently, it was demonstrated that deep learning models perform well for short-term load forecasting (STLF). However, prior research has demonstrated that the hybrid deep learning model outperforms the single model. We propose a hybrid neural network in this article that combines elements of a convolutional neural network (1D-CNN) and a long short memory network (LSTM) in novel ways. Multiple independent 1D-CNNs are used to extract load, calendar, and weather features from the proposed hybrid model, while LSTM is used to learn time patterns. This architecture is referred to as a CNN-LSTM network with multiple heads (MCNN-LSTM). To demonstrate the proposed hybrid deep learning model's superior performance, the proposed method is applied to Ireland's load data for single-step and multi-step load forecasting. In comparison to the widely used CNN-LSTM hybrid model, the proposed model improved single-step prediction by 16.73% and 24-step load prediction by 20.33%. Additionally, we use the Maine dataset to verify the proposed model's generalizability