Enhancing refinery heavy oil fractions analytical performance through real-time predicative modeling.

This study introduces a suite of robust models aimed to advance the determination of physiochemical properties in heavy oil refinery fractions. By integrating real-time analytical technique inside the refinery analysis, we have developed a single analyzer capable of employing six partial least square regression equations. These designed models enable to provide real-time prediction of critical petroleum properties, such as sulfur content, micro carbon residues (MCR), asphaltene content, heating value, and the concentrations of nickel and vanadium metals. Specifically tailored for heavy oil in refinery feeds with an American petroleum institute (API) gravity range of 3° to 32° and sulfur content of 2.8 to 5.5 wt%, the models streamline the analysis process within refinery operations, bridging the gap between catalytic and non-catalytic processes across refinery units. The accuracy of our physiochemical prediction models has been validated against American Society for Testing and Materials (ASTM) standards, demonstrating their capability to deliver precise real-time property values. This approach not only enhances the efficiency of refinery analysis but also sets a new standard for the monitoring and optimization of heavy oil processing in real-time approach.

Higher heating value estimation of wastes and fuels from ultimate and proximate analysis by using artificial neural networks.

Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22-55.55 MJ/kg. The relationships of carbon, hydrogen, and oxygen atomic ratios for fuel classes are illustrated by using ternary plots, and the effects of elemental composition on HHV was analyzed with the extensive dataset. Then, the ultimate (U) and ultimate & proximate (UP) datasets were utilized separately to estimate the HHV by using artificial neural networks (ANN). Hyperparameter optimization was carried out and the best performing ANNs were determined for each dataset, which yielded R2 values of 0.9719 and 0.9715, respectively. The results indicated that while ANNs trained by both datasets perform remarkably well, utilization of U dataset is sufficient for HHV estimation. Finally, the best performing ANN models for both U and UP datasets are given in a directly utilizable format enabling the accurate estimation of HHV of any fuel for optimization of fuel processing and waste management operations.

A comprehensive review on unleashing the power of hydrogen: revolutionizing energy systems for a sustainable future.

Population growth and environmental degradation are major concerns for sustainable development worldwide. Hydrogen is a clean and eco-friendly alternative to fossil fuels, with a heating value almost three times higher than other fossil fuels. It also has a clean production process, which helps to reduce the emission of hazardous pollutants and save the environment. Among the various production methodologies described in this review, biochemical production of hydrogen is considered more suitable as it uses waste organic matter instead of fossil fuels. This technology not only produces clean energy but also helps to manage waste more efficiently. However, the production of hydrogen obtained from this method is currently more expensive due to its early stage of development. Nevertheless, various research projects are underway to develop this method on a commercial scale.

Sludge deep dewatering by liquefied dimethyl ether: selection of operating conditions based on response surface methodology.

Sludge is an inevitable by-product of the sewage treatment process and its high moisture content poses significant challenges for its treatment and disposal. This study focuses on the technology of sludge deep dewatering using liquefied dimethyl ether (DME) and explores the relationship between operating parameters (DME/sludge ratio, extraction time and stirring speed) and the water content of the sludge after deep dewatering. After deep dewatering, the sludge's lower heating value (LHV) was significantly increased. The dehydrated filtrate is highly biodegradable and could be treated together with sewage. Based on the response surface method of central composite design, a second-order regression model of the above three variables and sludge water content as the response was established. Finally, the operating conditions diagram was drawn by target water content (36.96 wt.%) which meets the requirement of self-sustained incineration and model equation. This study provides a valuable perspective on sludge drying and fuelisation.

Turning trash into treasure: Torrefaction of mixed waste for improved fuel properties. A case study of metropolitan city.

Solid waste management is one of the biggest challenges of the current era. The combustible fractions in the waste stream turn out to be a good energy source if converted into refuse-derived fuel. Researchers worldwide are successfully converting it into fuel. However, certain challenges are associated with its application in gasifiers, boilers, etc. to co-fire it with coal. These include high moisture content, low calorific value, and difficulty to transport and store. The present study proposed torrefaction as a pretreatment of the waste by heating it in the range of 200 °C-300 °C in the absence of oxygen at atmospheric pressure. The combustible fraction from the waste stream consisting of wood, textile, paper, carton, and plastics termed as mixed waste was collected and torrefied at 225 °C, 250 °C, 275 °C, and 300 °C for 15 and 30 min each. It was observed that the mass yield and energy yield decreased to 45% and 62.96% respectively, but the energy yield tended to increase by the ratio of 1.39. Proximate analysis showed that the moisture content and volatile matter decreased for torrefied samples, whereas the ash content and fixed carbon content increased. Similarly, the elemental analysis revealed that the carbon content increased around 23% compared to raw samples with torrefaction contrary to hydrogen and oxygen, which decreased. Moreover, the higher heating value (HHV) of the torrefied samples increased around 1.3 times as compared to the raw sample. This pretreatment can serve as an effective solution to the current challenges and enhance refuse-derived fuel's fuel properties.

Co-processing of end-of-life wind turbine blades in portland cement production.

The objective of this paper is to evaluate the feasibility of co-processing wind turbine blade (WTB) material in cement manufacturing to provide an end-of-life means to divert the solid waste of decommissioned WTBs from landfills. Many WTBs consist primarily of glass fiber reinforced thermoset polymers that are difficult to recover or recycle. Portland cement is produced world-wide in large quantities, requiring immense quantities of raw materials (mostly calcium oxide and silicon oxide) and kiln temperatures approaching 1,450 °C. This work contributes analyses of WTB material composition, and predicts the energy provided through the combustible components of the WTBs and raw material contributions provided by incorporating the incombustible components of the WTBs to produce cement. Approximately 40 to 50 % of the WTB material will contribute as fuel to cement production, and approximately 50 to 60 % of the WTB material is expected to be incombustible. One tonne of WTB material can displace approximately 0.4 to 0.5 tonne of coal, while also contributing approximately 0.1 tonne of calcium oxide and 0.3 tonne of silicon oxide as raw material to the cement production process. The glass fiber WTB tested had an average boron content of 4.5 % in the ash. The effects of this high boron content on the cement and its production process should be evaluated. Co-processing WTBs in cement plants will slightly reduce combustion-related CO2 emissions due to avoided calcination. It seems feasible to co-process glass-fiber reinforced WTBs in cement production as WTBs provide suitable raw materials and compatible fuel for this process.

Modeling and optimization of compaction pressure, binder percentage and retention time in the production process of carbonized sawdust-based biofuel briquettes using response surface methodology (RSM).

The importance of parameters such as compaction pressure, binder percentage and retention time and their interaction in the production of carbonized briquettes for domestic or industrial use cannot be overestimated, as they have a considerable impact on the properties of the resulting briquettes. This study used Box-Behnken Response Surface Methodology (RSM) and Analysis Of Variance (ANOVA) to show how the above parameters and their interactions significantly influence the Higher Heating Value (HHV), ash content and Impact Resistance Index (IRI) of the biofuels obtained. The briquettes are characterized in accordance with American Society for Testing and Materials ASTM D-(5865 and 3172). IRI is determined by the drop test. The Niton XLT900s X-ray fluorescence spectrometer is used for mineralogical analysis. The peel starch used as a binder is characterized by the Association of Official Agricultural Chemists standard. This starch has a starch purity of 89.8 %, an HHV of 13974 kJ/kg, a protein content of 4.79 % and a sugar content of 1.3 %. The HHV of the biofuels ranged from 23783 to 26050 kJ/kg, their ash content from 2.86 to 5.24 %, and the IRI from 136.36 to 500 %. The significant effect of binder on these results is confirmed (p < 0.05). The Standard deviations of ± 21.425 kJ/kg, ± 0.021 % and ± 2.121 % were obtained between the experimental values and those of the mathematical models developed to predict HHV, ash content and IRI. The optimum parameters for industrial biofuel production correspond to a binder percentage of 10 %, a compaction pressure of 75 kPa and a retention time of 7.49 min. The experimental results under these conditions are: 25596 kJ/kg, 3.01 % and 375 % for HHV, ash content and IRI. In correlation with the absence of certain heavy metals, the study confirms that the briquettes produced are suitable for domestic use.

Modeling and simulation of Khat waste fast pyrolysis for energy recovery.

Bioenergy is now recognized to be capable of providing the vast majority of predicted future renewable energy supply. Biomass is currently considered a common and commonly used renewable energy source. This study depends upon the investigation of khat waste using Aspen Plus software, which is required for creating environmentally friendly energy sources capable of improving our access to energy and economic sustainability. The outcome of the study is to understand the characteristics of the pyrolysis process without conducting a time-consuming, expensive, and complex procedure. The results of the investigation will be useful in determining the best feedstock for the formation of biofuel. Aspen Plus software simulates several ash-free organic components, including carbon, oxygen, nitrogen, hydrogen, and sulfur, with results like 45.72 % for carbon, 5.84 % for hydrogen, 0.43 % for nitrogen, and 38.56 % for oxygen. The production of biofuel is affected by processing parameters such as temperature and total mass flow rate. During reactions with the same mass but different temperatures, the bio-oil declined from 600 °C to 800 °C, while the maximum gas emission climbed quickly and the biochar reduced. In addition, it was recovered from Khat waste and proved to have an energy efficiency of 80.75 % and a net energy capacity of 134.25 kW. In addition, the High heating value (HHV) can be obtained from Khat waste is 19.38 MJ/kg, and low heating (LHV) can be 18.12 MJ/kg. We have been able to realize it using the Institute of Gas Technology formula based on ultimate analysis. The results show that Khat produces more oil than other wastes. As a result, all Khat waste is naturally occurring and Khat waste usually contains less nitrogen and no sulfur when used as fuel, which is an air pollutant reducing and protecting the environment.

Machine learning prediction of higher heating value of biochar based on biomass characteristics and pyrolysis conditions.

The higher heating value of biochar is an important parameter for the utilization of biomass energy. In this work, extreme gradient boosting regression and artificial neural network were used to predict it based on the characteristics of biomass and pyrolysis conditions. Besides, empirical correlations were developed for comparison. Results showed that the extreme gradient boosting regression models showed better performance (R2 = 0.83-0.94). The shapley additive explanations and partial dependence plot indicated that lignin content and higher heating value of raw material were highly positively correlated with higher heating value of biochar, and found the better conditions such as pyrolysis temperature (>550 °C), lignin content (>40 wt%) for high-higher heating value biochar preparation. What's more, a program that predicted higher heating value of biochar was developed through PySimpleGUI library. It offered a new optimization idea for the directional preparation process of biochar.

Characterization of different solid fuels from waste for an advanced online fuel control system designed for large-scale incineration plants.

Despite many years of experience in the incineration of solid fuels from waste, the heterogeneity of solid fuels and their varying properties still pose a challenge for a stable and clean combustion in large-scale incineration plants. In modern facilities such as municipal waste incineration plants there still exists a lack of knowledge on the exact amount and calorific value of waste entering onto the grate. Based on the works of Warnecke et al. and Zwiellehner et al., in our project 'AdOnFuelControl', we determined the initial bulk density at the feed hopper by measuring the weight of the waste via the crane weigher and the volume via a high-performance 3D laser scanner. With the help of the determined bulk density, the lower heating value (LHV) and the compression in the feed hopper were calculated. All this information was integrated into the combustion control system, which provided a high potential for an optimized operation of the plant. In this article, six different fuels (fresh and aged municipal solid waste, refuse-derived fuel (fluff), refuse-derived fuel (fine grain), waste wood and dried, grained sewage sludge) were examined for the elemental composition, the LHV, fuel-specific parameters and the compression behaviour. In addition, initial tests with the 3D laser scanner as well as formulas for the calculation of the density in the feed hopper were presented. Based on the results of the experiments, the chosen approach seems very promising for optimized combustion control in large-scale incineration plants. As a next step, the gained knowledge and technology should be integrated in the municipal waste incineration plant.

Reliability Analysis of HHV Prediction Models for Organic Materials Using Bond Dissociation Energies.

The purpose of this study is to analyze the reliability of predictive models for higher heating values related to organic materials. A theoretical model was developed, which utilizes bond dissociation energies (BDEs) to establish correlations between elemental composition and calorific values. Our analysis indicates that the energy contribution of one mole of hydrogen atoms is approximately equal to -144.4 kJ mol-1. Further investigation reveals significant variations in the bond dissociation energies of carbon atoms within organic compounds, resulting in a range of energy outputs from -414.30 to -275.34 kJ mol-1 per mole of carbon atoms. The presence of oxygen atoms in organic compounds has a negative impact on the magnitude of combustion heat, with values ranging from 131.1 to 207.17 kJ mol-1. The combustion mechanism imposes certain constraints, leading to the equation HHVg = -31.34·[C] - 144.44·[H] + 10.57·[O] for organic compounds. Based on the parameter sensitivity analysis, the coefficient associated with carbon mass fraction exhibits a significantly greater impact on result prediction accuracy, demonstrating a sensitivity value of 92.65%. The results of further analysis indicate that empirical correlations involving the mass fractions of the elements N and S in lignocellulosic materials may be prone to over-fitting, with sensitivity indices of 1.59% and 0.016%, respectively.

Thermal evaluation of plant biomass from the phytostabilisation of soils contaminated by potentially toxic elements.

The combination of phytoremediation of soils contaminated by potentially toxic elements with energy production by combustion of the generated biomass can be a sustainable land management option, combining the production of renewable bioenergy with soil restoration while minimising energy consumption and CO2 emission. In this work, plant biomass from phytoremediation of soils contaminated by potentially toxic elements was studied as solid biofuel for combustion by thermal analysis and biomass composition. Six plant species were grown in two soils with differing degrees of contamination: Brassica juncea, Cynara cardunculus, Atriplex halimus, Nicotiana glauca, Dittrichia viscosa, Retama sphaerocarpa and Salvia rosmarinus. The composition of the plant biomass was characterised chemically and thermogravimetric analyses were performed for the mass loss (TG), derivative curves of mass loss (DTG) and temperature difference (DTA) signal. The cellulose concentration correlated with the parameters of the thermal analysis in the low temperature range (150-350 °C), while lignin correlated with the thermal parameters of the second peak in the high temperature range. Salvia rosmarinus and R. sphaerocarpa showed the best combustion characteristics according to the thermal profile and mineral residue results. The accumulation of potentially toxic elements in B. juncea grown in heavily contaminated soil led to a higher amount of residue at 750 °C, with a global activation energy lower than the one obtained when this species was grown in a soil with lower contamination. Therefore, the most beneficial combination of soil phytoremediation and energy production (combustion) that can be suggested would depend on the level of soil contamination: in heavily contaminated soil, phytostabilisation using R. sphaerocarpa and S. rosmarinus; in slightly contaminated soil, B. juncea due to its high energy of activation, although the concentrations of potentially toxic elements in the residue must be controlled, as well as possible particulate matter emissions during combustion.

Characterization of charcoal briquettes produced from blending rice straw and banana peel.

Blending rice straw and banana peel to form briquettes pronounced their properties. The addition of banana peel resulted in high physical and combustion properties. Different briquette samples were procured by blending varying the loads of rice straw and banana peel at the ratios of 100:0, 90:10, 80:20, 70:30, 60:40, 50:50, 40:60, 30:70 20:80, 10:90, and 0:100, respectively, using cassava starch as the binder. The properties of the sample briquettes were investigated, and it was noted that the physical properties of briquettes produced using rice straw and banana peel at the ratios of 30:70, 20:80, and 10:90 had high bulk density of 610-660 kg/m3 and compressed density of 768-831 kg/m3. The combustion properties of low moisture and ash content were approximately 9.7%-10.6% and 16.5%-18.2%, respectively; volatile matter was 39.7%-44.0%; fixed carbon was 28.9%-32.4%; and high heating value was 20.98-21.26 MJ/kg. Residual waste from Community Enterprise of Crispy Banana Chips was used for the production of more effective briquettes and as a natural alternative fuel to expand the global eco-friendly charcoal briquette market.

A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass.

The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proximate and ultimate analyses is not linear, nonlinear models might be a better alternative. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg-Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.

Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning.

Biochar is a high-carbon-content organic compound that has potential applications in the field of energy storage and conversion. It can be produced from a variety of biomass feedstocks such as plant-based, animal-based, and municipal waste at different pyrolysis conditions. However, it is difficult to produce biochar on a large scale if the relationship between the type of biomass, operating conditions, and biochar properties is not understood well. Hence, the use of machine learning-based data analysis is necessary to find the relationship between biochar production parameters and feedstock properties with biochar energy properties. In this work, a rough set-based machine learning (RSML) approach has been applied to generate decision rules and classify biochar properties. The conditional attributes were biomass properties (volatile matter, fixed carbon, ash content, carbon, hydrogen, nitrogen, and oxygen) and pyrolysis conditions (operating temperature, heating rate residence time), while the decision attributes considered were yield, carbon content, and higher heating values. The rules generated were tested against a set of validation data and evaluated for their scientific coherency. Based on the decision rules generated, biomass with ash content of 11-14 wt%, volatile matter of 60-62 wt% and carbon content of 42-45.3 wt% can generate biochar with promising yield, carbon content and higher heating value via a pyrolysis process at an operating temperature of 425°C-475°C. This work provided the optimal biomass feedstock properties and pyrolysis conditions for biochar production with high mass and energy yield.

Interpretable machine learning assisted spectroscopy for fast characterization of biomass and waste.

The combination of machine learning and infrared spectroscopy was reported as effective for fast characterization of biomass and waste (BW). However, this characterization process is lack of interpretability towards its chemical insights, leading to less satisfactory recognition for its reliability. Accordingly, this paper aimed to explore the chemical insights of the machine learning models in the fast characterization process. A novel dimensional reduction method with significant physicochemical meanings was thus proposed, where the high loading spectral peaks of BW were selected as input features. Combined with functional groups attribution of these spectral peaks, the machine learning models established based on the dimensionally reduced spectral data could be explained with clear chemical insights. The performance of classification and regression models between the proposed dimensional reduction method and principal component analysis method was compared. The influence mechanism of each functional group on the characterization results were discussed. CH deformation, CC stretch & CO stretch and ketone/aldehyde CO stretch played essential roles in C, H/ LHV and O prediction, respectively. The results of this work demonstrated the theoretical fundamentals of the machine learning and spectroscopy based BW fast characterization method.

Banana pseudo-stem biochar derived from slow and fast pyrolysis process.

This study evaluated the properties of banana pseudo-stem (BPS) biochar derived from two different types of pyrolysis. The fast pyrolysis experiment was performed using a worktable-scale fluidized-bed reactor, while a bench-scale fixed-bed reactor was used in the slow pyrolysis experiment. The preliminary analysis shows that the feedstock contains 80.6 db wt% of volatile matter, 12.5 db wt% of ash and 33.6% of carbon content. Biochar yield reduces as the pyrolysis temperature elevates for both pyrolysis experiments. Fast pyrolysis yields a higher percentage of biochar (40.3%) than biochar yield obtained from the slow pyrolysis experiment (34.9 wt%) at a similar temperature of 500 °C. The evaluation of biochar derived at 500 °C shows that the biochar obtained from the slow pyrolysis process has higher carbon content, heating value, and surface area with lower ash content. Meanwhile, FESEM images show significant differences in surface morphology and the number of pores for biochar derived from fast and slow pyrolysis. These findings indicate the potential and suitability of BPS biochar derived from the slow pyrolysis process in applications such as soil amelioration and solid biofuel.

Hydrogen-rich gas production from disposable COVID-19 mask by steam gasification.

Globally, the demand for masks has increased due to the COVID-19 pandemic, resulting in 490,201 tons of waste masks disposed of per month. Since masks are used in places with a high risk of virus infection, waste masks retain the risk of virus contamination. In this study, a 1 kg/h lab-scale (diameter: 0.114 m, height: 1 m) bubbling fluidized bed gasifier was used for steam gasification (temperature: 800 °C, steam/carbon (S/C) ratio: 1.5) of waste masks. The use of a downstream reactor with activated carbon (AC) for tar cracking and the enhancement of hydrogen production was examined. Steam gasification with AC produces syngas with H2, CO, CH4, and CO2 content of 38.89, 6.40, 21.69, and 7.34 vol%, respectively. The lower heating value of the product gas was 29.66 MJ/Nm3 and the cold gas efficiency was 74.55 %. This study showed that steam gasification can be used for the utilization of waste masks and the production of hydrogen-rich gas for further applications.

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR).

Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation and energy demand duality. However, waste diversity makes their management a serious challenge. Among their categories, biomass waste valorization is an attractive solution energy regarding its low cost and raw materials availability. Nevertheless, the knowledge of biomass waste characteristics, such as composition and energy content, is a necessity. In this research, new models are developed to estimate biomass wastes higher heating value (HHV) based on the ultimate analysis using linear regression and artificial neural network (ANN). The quality-measure of the two models for new dataset was evaluated with statistical metrics such as coefficient of correlation (R), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The methods developed in this work provided attractive accuracies comparing to other literature models. Additionally, it is found that the ANN, as machine learning method, is the best model for biomass HHV prediction (R = 0.75377, RMSE = 1.17527, MAE = 0.93315 and MAPE = 5.73%). Therefore, obtained results can be widely employed to design and optimize the reactors of combustion. In fact, the developed ANN software is a simple and accurate tool for HHV estimation based on ultimate analysis. Indeed, ANN is one of the most applicable and widely used software in the field of waste-to-energy.

Variability on the energy properties of charcoal and charcoal briquettes for barbecue.

In recent years there has been a strong increase in interest in the world of barbecues and outdoor cooking in high-income countries. Referring to FAO data, an exponential growth in imports of charcoal was observed in Europe and North America. Italy is one of the major European consumers and importers. On the market it is possible to find material with different characteristics and origins. However, analysis aimed at ascertaining the quality of the material are poorly performed. This research aimed to analyze the energy properties of charcoal commonly available on the Italian market. Twenty-four bags of charcoal and charcoal briquettes were analyzed. Eighteen samples represent the products most easily found on the market, in stores and on websites. In addition, six samples were supplied directly by the producer/importing company. The samples were grouped according to the continent of origin of the material (Europe, North-Central America and South America). Charcoal briquette samples were included together in a group. Referring to the ISO 17225-1 standard, the moisture content, ash content, heating value, volatile matter and fixed carbon were determined. Except for the moisture content, the results of the tests performed on all parameters show a strong variability both between different groups and within the same group. In detail, the European charcoal samples show characteristics more suitable for their use in barbecues. These have the highest values of fixed carbon and heating value and, at the same time, low values of ash and volatile matter. On the contrary, charcoal briquettes have less suitable characteristics for barbecue. The work also highlighted some gaps in the reference standard relating to laboratory analyses. To ensure careful control of the qualitative characteristics of the products on the market, it is necessary to promote the creation of a quality brand.