• Energy Research

The suitability of recycled poly(ethylene terephthalate) (R-PET) for 3D-printing applications was evaluated by studying the melt flow characteristics of the polymer. R-PET is known to experience significant deterioration in its mechanical properties when recycled due to molecular weight loss that results from reprocessing. Lower molecular weight hinders R-PET from being 3D-printable due to low viscosity and melt strength. The hypothesis was that R-PET can be modified with reasonable effort and resources to a 3D-printable thermoplastic if the low viscosity problem is tackled. Higher viscosity will enhance both the melt strength and the melt flow characteristic of the polymer, making it more suitable for processing and 3D printing. Reactive extrusion was selected as the method for modifying the polymer to achieve the objective via a coupling reaction with chain extender PMDA (pyromellitic dianhydride). A decrease in the melt flow index (MFI) from 90 to 1.2 (g/10 min) was recorded when PMDA was added at 0.75 wt% which lowered the MFI of modified R-PET to a comparable value to commercial 3D-printing filaments. Furthermore, FT-IR analysis was performed to investigate the chemical composition of the product. Finally, a 3D-printing filament was made from the modified R-PET by mimicking the main processing stations that exist in the filament-making process, which are the extrusion stage, water bath cooling stage and spooling stage. With 0.75 wt% PMDA, the melt strength was satisfactory for pulling the filament and, therefore, a filament with on-spec dimension was produced. Finally, a small object was successfully 3D-printed using the filament product at a minimum recommended temperature of 275 °C.

The integration of smart city technologies into waste management is a challenging field for decision makers due to its multivariate, multi-limiting, and multi-stakeholder structure, despite its contribution to the ecological and economic sustainability understanding of cities. The success of smart sustainable waste management strategies depends on many environmental, technical, economic, and social variables, and many stakeholders are involved in these processes. Using fuzzy multi-criteria decision-making (MCDM) methods helps decision makers determine effective, affordable, and acceptable smart waste management strategies. Although MCDM methods are widely used in various environmental engineering applications, the determination of smart sustainable waste management strategies using these methods has not yet received enough attention in the literature. This study aims to contribute to this gap in the literature by evaluating four different smart waste management strategies using a hybrid fuzzy MCDM method. The performance of the proposed strategy alternatives according to fifteen sub-criteria (under four main criteria selected from the literature) was evaluated using a combined application of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to obtain the ideal solution (fuzzy TOPSIS). For this evaluation, the subjective opinions of ten different experts working in academia, in the private sector, or in the public sector were obtained using prepared questionnaires. As a result, the sub-criteria of fewer atmospheric emissions (0.42), operational feasibility (0.64), initial investment costs (0.56), and increased awareness of sustainable cities (0.53) had the highest weight values in their main criteria groups. The performance ranking of the alternatives according to the closeness coefficient (CCi) values was obtained as A2 (0.458) > A3 (0.453) > A4 (0.452) > A1 (0.440), with A3 being slightly ahead of A4 due only to a 0.001 higher CCi value. To test the reliability and stability of the obtained performance ranking results, a sensitivity analysis was also performed using eighteen different scenarios, in which the weights of the different sub-criteria were increased by 25% or decreased by 50%, or they were assumed to be 1 and 0, or all sub-criteria in the same group had equal weight values. Since the performance ranking of the alternatives did not change, the ranking obtained at the beginning was found to be robust against the sub-criterion weight changes.

© 2022, The Author(s), under exclusive licence to Springer Nature B.V.This study was conducted to explore the biochemical methane potential of three different agricultural biomass sources including sunflower (Helianthus annuus L.) meal, Jerusalem artichoke (Helianthus tuberosus L.), and food waste (mixture of cooked and uncooked food) under the identical mesophilic biodegradation conditions. A synergistic quantitative analysis was explicitly introduced to appraise the biochemical methane potentials and life cycle environmental impacts (in terms of greenhouse gases and various midpoint categories) of these agro-industrial wastes. Three different sigmoidal microbial growth curve models (modified Gompertz equation, transference function (reaction curve-type model), and logistic function) were also implemented to determine the bio-kinetics of the studied substrates using a novel MATLAB®-based hybrid optimization procedure. The experimental results indicated that the ultimate methane yield (915 mL CH4/g VS) achieved for the canteen-originated food waste was 1.46 and 1.37 times higher than the yields obtained from Jerusalem artichoke and sunflower meal, respectively. The logistic function simulated by a Quasi-Newton cubic line search algorithm best described the bio-kinetics of all agro-industrial substrates with the minimum nonlinear iterations and function counts. According to the life cycle assessment, the use of Jerusalem artichoke in biogas production showed the worst performance in environmental terms. However, no obvious difference was found between the use of food waste and sunflower meal. Graphical Abstract: [Figure not available: see fulltext.]

This study focuses on predicting the output power of wind turbines (WTs) using wind speed and WT operational characteristics. The main contribution of this work is a model identification method based on an adaptive neuro-fuzzy inference system (ANFIS) through multi-source data fusion on a moving window (MoW). The proposed ANFIS-MoW-based approach was applied to data in different time series windows, namely the very short-term, short-term, medium-term, and long-term time horizons. Data collected from a 30-MW wind farm on the west coast of Nouakchott (Mauritania) were used in the computational analysis. Compared to nonparametric models from the literature and models employing artificial intelligence machine learning techniques, the proposed ANFIS-MoW model demonstrated superior predictions for the output power of the WT with the fusion of very little data collected from different WTs. Moreover, for various time series windows (TSW) and meteorological conditions, additional benchmarking demonstrated that the ANFIS-MoW-based method outperformed five existing ANFIS-based models, including grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), fuzzy c-means clustering (ANFIS-FCM), genetic algorithm (ANFIS-GA), and particle swarm optimization (ANFIS-PSO). The results indicated that the suggested methodology is a promising soft-computing tool for accurately estimating the WT output power for WTs' sustainability through better control of their operation.Wind turbine; Time series horizon; Adaptive neuro-fuzzy inference system; Movingwindow approach; Power prediction

A new approach for sizing a hybrid solar-PV-battery and biogas generator for power generation was suggested in this study, based on the variation of energy resources and the load profile. Biogas has enormous potential and numerous economic and social advantages. In this respect, the monthly solar radiation, temperature, and biogas produced from biomass resources were used as model inputs. This study considers the total annualized of the components for the feasibility analysis. HOMER Pro® software-based outcomes in the proposed methodology revealed that the proposed solar biogas hybrid system was sufficient to meet the load requirements of the village (Ain Farba village in the Hodh El Gharbi area located in the northeast of the Mauritania country) with a net present cost over the 25-year lifespan of 61,144 $ and the energy cost was determined to be 0.0473 $/kWh.

Green hydrogen is becoming increasingly popular, with academics, institutions, and governments concentrating on its development, efficiency improvement, and cost reduction. The objective of the Ministry of Petroleum, Mines, and Energy is to achieve a 35% proportion of renewable energy in the overall energy composition by the year 2030, followed by a 50% commitment by 2050. This goal will be achieved through the implementation of feed-in tariffs and the integration of independent power generators. The present study focused on the economic feasibility of green hydrogen and its production process utilizing renewable energy resources on the northern coast of Mauritania. The current investigation also explored the wind potential along the northern coast of Mauritania, spanning over 600 km between Nouakchott and Nouadhibou. Wind data from masts, Lidar stations, and satellites at 10 and 80 m heights from 2022 to 2023 were used to assess wind characteristics and evaluate five turbine types for local conditions. A comprehensive techno-economic analysis was carried out at five specific sites, encompassing the measures of levelized cost of electricity (LCOE) and levelized cost of green hydrogen (LCOGH), as well as sensitivity analysis and economic performance indicators. The results showed an annual average wind speed of 7.6 m/s in Nouakchott to 9.8 m/s in Nouadhibou at 80 m. The GOLDWIND 3.0 MW model showed the highest capacity factor of 50.81% due to its low cut-in speed of 2.5 m/s and its rated wind speed of 10.5 to 11 m/s. The NORDEX 4 MW model forecasted an annual production of 21.97 GWh in Nouadhibou and 19.23 GWh in Boulanoir, with the LCOE ranging from USD 5.69 to 6.51 cents/kWh, below the local electricity tariff, and an LCOGH of USD 1.85 to 2.11 US/kg H2. Multiple economic indicators confirmed the feasibility of wind energy and green hydrogen projects in assessed sites. These results boosted the confidence of the techno-economic model, highlighting the resilience of future investments in these sustainable energy infrastructures. Mauritania’s north coast has potential for wind energy, aiding green hydrogen production for energy goals.

This computational study explores the potential of several soft-computing techniques for wind turbine (WT) output power (kW) estimation based on seven input variables of wind speed (m/s), wind direction (°), air temperature (°C), pitch angle (°), generator temperature (°C), rotating speed of the generator (rpm), and voltage of the network (V). In the present analysis, a nonlinear regression-based model (NRM), three decision tree-based methods (random forest (RF), random tree (RT), and reduced error pruning tree (REPT) models), and multilayer perceptron-based soft-computing approach (artificial neural network (ANN) model) were simultaneously implemented for the first time in the prediction of WT output power (WTOP). To identify the top-performing soft computing technique, the applied models’ predictive success was compared using over 30 distinct statistical goodness-of-fit parameters. The performance assessment indices corroborated the superiority of the RF-based model over other data-intelligent models in predicting WTOP. It was seen from the results that the proposed RF-based model obtained the narrowest uncertainty bands and the lowest quantities of increased uncertainty values across all sets. Although the determination coefficient values of all competitive decision tree-based models were satisfactory, the lower percentile deviations and higher overall accuracy score of the RF-based model indicated its superior performance and higher accuracy over other competitive approaches. The generator’s rotational speed was shown to be the most useful parameter for RF-based model prediction of WTOP, according to a sensitivity study. This study highlighted the significance and capability of the implemented soft-computing strategy for better management and reliable operation of wind farms in wind energy forecasting.

A high pH, low solubility of bound plant nutrients, and negative impacts on microbial communities are common drawbacks of biomass ash (BA) vermicomposting. In this study, nutrient-rich BA mixed with cow manure was tested at three different application rates to obtain final nitrogen (N), phosphorus (P), and potassium (K) contents of 3.5%, 7.0%, and 10.0% for bio-based fertilizers via vermicomposting. The results showed that all BA blends made with cow manure increased fermentation temperatures and allowed successful worm activity during the subsequent vermicomposting phase. The order of indicator enzyme activities in all vermicomposting samples was urease (220 μg NH4 g−1 h−1) > β-glucosidase (95 μg PNP g−1 h−1) > alkaline phosphatase (91 μg PNP g−1 h−1) > arylsulfatase (83 μg PNP g−1 h−1) > acid phosphatase (60 μg PNP g−1 h−1). As an indicator of nutrient bioavailability, high correlations were observed between enzyme activities and microbial diversity in vermicompost samples. Determination coefficients (R2) obtained from multiple linear regressions between enzyme activities and bacterial population for T0, T1, T2, and T3 were determined as 0.90, 0.65, 0.73, and 0.90, respectively. According to a novel metagenome-based approach proposed within the scope of the present study, the stimulatory effects of Flavobacteriales, Burkholderiales, Saccharimonadales, and Pseudomonadales on enzyme activities for the nutrient solubility were found to be significant and positive. The findings of this study demonstrated that worm composting could be a sustainable bio-based technology for the production of slow-release fertilizer from nutrient-rich waste material.