Search Results (85)

The polymer industry gained increasing importance due to the ability of polymers to replace traditional materials such as wood, glass, and metals in various applications, offering advantages such as high strength-to-weight ratio, corrosion resistance, and ease of fabrication. Among key performance indicators, melt flow rate (MFR) plays a crucial role in determining polymer quality and processability. However, conventional offline laboratory methods for measuring MFR are time-consuming and unsuitable for real-time quality control in industrial settings. To address this challenge, the study proposes a leveraging artificial intelligence with machine learning-based melt flow rate prediction for polymer properties analysis (LAIML-MFRPPPA) model. A dataset of 1044 polymer samples was used, incorporating six input features such as reactor temperature, pressure, hydrogen-to-propylene ratio, and catalyst feed rate, with MFR as the target variable. The input features were normalized using min–max scaling. Two ensemble models—kernel extreme learning machine (KELM) and random vector functional link (RVFL)—were developed and optimized using the pelican optimization algorithm (POA) for improved predictive accuracy. The proposed method outperformed traditional and deep learning models, achieving an R² of 0.965, MAE of 0.09, RMSE of 0.12, and MAPE of 3.4%. A SHAP-based sensitivity analysis was conducted to interpret the influence of input features, confirming the dominance of melt temperature and molecular weight. Overall, the LAIML-MFRPPPA model offers a robust, accurate, and deployable solution for real-time polymer quality monitoring in manufacturing environments. Full article

This two-part study assesses the impact of biogas inhibition on large-scale waste-to-energy anaerobic digestion (WtE-AD) plants through techno-economic and life cycle assessment approaches. The first part addresses technical and economic aspects. An anaerobic co-digestion system using vegetable waste (FVW) and meat waste (MW) was operated at laboratory scale in a semi-continuous regime with daily feeding to establish a stable process and induce programmed failures causing methanogenic inhibition, achieved by removing MW from the reactor feed and drastically reducing the protein content. Experimental data, combined with bioprocess scale-up models and cost engineering methods, were then used to evaluate the effect of inhibition periods on the profitability of large-scale WtE-AD processes. In the experimental stage, the stable process achieved a yield of 521.5 ± 21 mL CH₄ g⁻¹ volatile solids (VS) and a biogas productivity of 0.965 ± 0.04 L L⁻¹ d⁻¹ (volume of biogas generated per reactor volume per day), with no failure risk detected, as indicated by the volatile fatty acids/total alkalinity ratio (VFA/TA, mg VFA L⁻¹/mg L⁻¹) and the VFA/productivity ratio (mg VFA L⁻¹/L L⁻¹ d⁻¹), both recognized as effective early warning indicators. However, during the inhibition period, productivity decreased by 64.26 ± 11.81% due to VFA accumulation and gradual TA loss. With the progressive reintroduction of the FVW:MW management and the addition of fresh inoculum to the reaction medium, productivity recovered to 96.7 ± 1.70% of its pre-inhibition level. In WtE-AD plants processing 60 t d⁻¹ of waste, inhibition events can reduce net present value (NPV) by up to 40.2% (from 0.98 M USD to 0.55 M USD) if occurring once per year. Increasing plant capacity (200 t d⁻¹), combined with higher revenues from waste management fees (99.5 USD t⁻¹) and favorable electricity markets allowing higher selling prices (up to 0.23 USD kWh⁻¹), can enhance resilience and offset inhibition impacts without significantly compromising profitability. These findings provide policymakers and industry stakeholders with key insights into the economic drivers influencing the competitiveness and sustainability of WtE-AD systems. Full article

As a result of the catalytic decomposition of H₂O₂, hydroxyl radicals are produced. Hydroxyl radicals are strong oxidants and effectively inactivate bacteria, ensuring water disinfection without toxic chlorinated organic by-products. The kinetics of bacterial inactivation were studied in a laboratory-scale flow catalytic reactor. A granular cobalt ferrite catalyst was thoroughly characterized using XRD and XRF techniques, SEM with EDS, and Raman spectroscopy. At lower H₂O₂ concentrations, H₂O₂ decomposition follows first-order reaction kinetics. At higher H₂O₂ concentrations, the obtained kinetics lines suggest that the reaction order increases. The kinetics of bacterial inactivation in the developed flow reactor depends largely on the initial number of bacteria. The initial bacterial concentrations in laboratory tests were within the range typical of real river water. A regression model was developed that relates the degree of bacterial inactivation to the initial number of bacteria, the initial H₂O₂ concentration, and the contact time of water with the catalyst. Full article

Automated monitoring of the coolant temperature can enable autonomous operation of generation IV reactors (GenIV), thus reducing their operating and maintenance costs. Automation can be accomplished with machine learning (ML) models trained on historical sensor data. However, the performance of ML usually depends on the availability of large amount of training data, which is difficult to obtain for GenIV, as this technology is still under development. We propose the use of transfer learning (TL), which involves utilizing knowledge across different domains, to compensate for this lack of training data. TL can be used to create pre-trained ML models with data from small-scale research facilities, which can then be fine-tuned to monitor GenIV reactors. In this work, we develop pre-trained Transformer and long short-term memory (LSTM) networks by training them on temperature measurements from thermal hydraulic flow loops operating with water and Galinstan fluids at room temperature at Argonne National Laboratory. The pre-trained models are then fine-tuned and re-trained with minimal additional data to perform predictions of the time series of high temperature measurements obtained from the Engineering Test Unit (ETU) at Kairos Power. The performance of the LSTM and Transformer networks is investigated by varying the size of the lookback window and forecast horizon. The results of this study show that LSTM networks have lower prediction errors than Transformers, but LSTM errors increase more rapidly with increasing lookback window size and forecast horizon compared to the Transformer errors. Full article

The increasing presence of persistent pollutants in industrial wastewater underscores the shortcomings of conventional treatment methods, prompting the adoption of advanced oxidation processes (AOPs) for sustainable water remediation. This review examines the development of AOPs, focusing on their ability to produce hydroxyl radicals and reactive oxygen species (ROS) to mineralize complex pollutants. Homogeneous systems such as Fenton’s reagent show high degradation efficiency. However, challenges like pH sensitivity, catalyst recovery issues, sludge generation, and energy-intensive operations limit their scalability. Heterogeneous catalysts, such as TiO₂-based photocatalysts and Fe₃O₄ composites, offer improved pH adaptability, visible-light activation, and recyclability. Emerging innovations like ultraviolet light emitting diode (UV-LED)-driven systems, plasma-assisted oxidation, and artificial intelligence (AI)-enhanced hybrid reactors demonstrate progress in energy efficiency and process optimization. Nevertheless, key challenges remain, including secondary byproduct formation, mass transfer constraints, and economic feasibility for large-scale applications. Integrating AOPs with membrane filtration or biological treatments enhances treatment synergy, while advances in materials science and computational modeling refine catalyst design and reaction mechanisms. Addressing barriers in energy use, catalyst durability, and practical adaptability requires multidisciplinary collaboration. This review highlights AOPs as pivotal solutions for water security amid growing environmental pollution, urging targeted research to bridge gaps between laboratory success and real-world implementation. Full article

Catalytic reactor engineering bridges the active-site scale and the industrial-reactor scale, with kinetics as the primary bottleneck in scale-up. The main challenge in kinetics is conceptualizing the active site and formulating the reaction mechanism, leading to multiple approaches without clear guidance on their reliability for industrial-reactor design. This work assesses different approaches to active-site conceptualization and reaction-mechanism formulation for selective oxidation over a complex multi-metal catalyst. It integrates atomistic-scale insights from periodic Density Functional Theory (DFT) calculations into kinetic-model development. This approach contrasts with the macroscopic classical method, which treats the catalyst as a black box, as well as with alternative atomistic methods that conceptualize the active site as a single metal atom on different catalytic-surface regions. As a case study, this work examines ethane oxidative dehydrogenation to ethylene over the multi-metal oxide catalyst MoVTeNbO, which has a complex structure. This analysis provides insights into the ability of DFT to accurately describe reactions on such materials. Additionally, it compares DFT predictions to experimental data obtained from a non-idealized MoVTeNbO catalyst synthesized and assessed under kinetic control at the laboratory scale. The findings indicate that while the black-box active-site conceptualization best describes observed trends, its reaction mechanism and parameters lack reliability compared to DFT calculations. Furthermore, atomistic active-site conceptualizations lead to different parameter sets depending on how the active site and reaction mechanism are defined. Unlike previous studies, our approach determines activation-energy profiles within the range predicted by DFT. The resulting kinetic model describes experimental trends while maintaining phenomenological and statistical reliability. The corrections required for primary parameters remain below 20 kJ mol${}^{-1}$, consistent with the inherent uncertainties in DFT calculations. In summary, this work demonstrates the feasibility of integrating atomistic insights into kinetic modeling, offering different perspectives on active-site conceptualization and reaction-mechanism formulation, paving the way for future studies on rational catalyst and industrial-reactor design. Full article

This paper aims to explore both experimental and modeling anaerobic digestion (AD) processes as innovative methods for managing the substantial quantities of spent coffee grounds (SCG) generated in Algeria, transforming them into valuable renewable energy sources (biogas/methane). AD of SCG, while promising, is hindered by its complex lignocellulosic structure, which poses a significant challenge. This study investigates the efficacy of hydrogen peroxide (H₂O₂) pretreatment in addressing this issue, with a particular focus on enhancing biogas and methane production. The AD of SCG was conducted over a 46-day period, and the impact of H₂O₂ pretreatment was evaluated using laboratory-scale batch anaerobic reactors. Four different concentrations of H₂O₂ (0.5, 1, 2, and 4% H₂O₂ w/w) were studied in mesophilic conditions (37 ± 2) for 24 h at room temperature, providing basic data on biogas and methane production. The results showed a significant increase in soluble oxygen demand (SCOD) and total sugar solubilization in the range of 555.96–713.02% and 748.48–817.75%, respectively. The optimal pretreatment was found to be 4% H₂O₂ w/w resulting in 16.28% and 16.93% improvements in biogas and methane yield over the untreated SCG. Further, while previous research has established oxidative pretreatment efficacy, this study uniquely combines the empirical analysis of H₂O₂ pretreatment with a detailed kinetic modeling approach using the modified Gompertz (MG) and logistic function (LF) models to estimate kinetic parameters and determine the accuracy of fit. The MG model showed the most accurate prediction, thus making the present investigation a contribution to understanding the performance of the AD system under oxidative pretreatment and designing and scaling up new systems with predictability. These findings highlight the potential of H₂O₂-pretreated SCG as a more efficient and readily available resource for sustainable waste management and renewable energy production. Full article

Solid-state anaerobic digestion (SS-AD) is a promising technology for treating organic waste and producing renewable energy. This study explores the feasibility of using 3D printing to rapidly design cost-effective laboratory-scale digesters for optimization experiments. Batch reactors were designed using fused deposition modeling (FDM) with polylactic acid (PLA) and stereolithography (SLA) with High Temp V2 resin. PLA had a negligible impact on methane yields, while raw SLA resin positively influenced methanogenic potential, likely due to residual isopropanol used in post-processing, causing a 19% increase in CH4 yield. The performance of the 3D-printed reactors was compared to that of a conventionally machined PMMA reactor using cattle manure as a substrate, showing comparable methane yields and process stability. Three-dimensional printing technologies have demonstrated remarkable efficiency in designing laboratory-scale digesters, with a 70% cost reduction for SLA technology and an 80% reduction in design time compared to conventional reactors designed by plastics processing, while maintaining comparable biogas production. FDM technologies with PLA have shown that they are not suitable for these uses. This study demonstrates the potential of additive manufacturing to accelerate SS-AD research and development. However, care must be taken in material selection and post-processing to avoid introducing experimental bias. Full article

Background and Objectives: Drug delivery systems (DDSs) offer efficient treatment solutions to challenging diseases such as central nervous system (CNS) diseases by bypassing biological barriers such as the blood–brain barrier (BBB). Among DDSs, polymeric nanoparticles (NPs), particularly poly(lactic-co-glycolic acid) (PLGA) NPs, hold an outstanding position due to their biocompatible and biodegradable qualities. Despite their potential, the translation of PLGA NPs from laboratory-scale production to clinical applications remains a significant challenge. This study aims to address these limitations by developing scalable PLGA NPs and evaluating their potential biological applications. Methods: We prepared blank and model-protein-loaded (albumin–FITC and wheat germ agglutinin-488 (WGA-488)) fluorescent PLGA NPs using the traditional double-emulsion method combined with the micro-spray-reactor system, a novel approach that enables fine particle production enabling scale-up applications. We tested the biocompatibility of the NPs in living RPMI 2650 and neuroblastoma cell lines, as well as their trafficking and uptake. Release kinetics of the encapsulated proteins were investigated through confocal microscopy and in vitro release studies, providing insights into the stability and functionality of the released proteins. Results: The formulation demonstrated sustained and prolonged protein release profiles. Importantly, cellular uptake studies revealed that the NPs were not internalized. Furthermore, encapsulated WGA-488 protein retained its functional activity after release, validating the integrity of the encapsulation and release processes. Conclusions: The proof-of-concept study on NP manufacturing and an innovative drug trafficking and release approach can bring new perspectives on scalable preparations of PLGA NPs and their biological applications. Full article

This paper reports the results of a study on the significance of the inertization system configuration of a laboratory-scale fixed bed batch reactor with regard to the yield of pyrolysis oil and reactor conversion. Two typical reactor inertization systems were investigated depending on whether the carrier gas (nitrogen in this study) was added from the top or from the bottom of the reactor. Polypropylene (PP) packaging waste (100 g) was used as a model sample. A factorial experimental design was adopted for one categorical parameter, the arrangement of parts of the reactor inertization system. All experiments were conducted at 475 °C, with a carrier gas flow rate of 0.1 L/min and a reaction time of 90 min. Statistical analysis and processing of the results showed that the configuration of the inertization system had a remarkable impact on the pyrolysis oil and gas yield, while its impact on the overall reactor conversion was negligible. When applying the two observed methods of reactor inertization, the average yields of pyrolysis oil and gas differed by 1.7% and 1.8%, respectively. All of the applied statistical treatments had a significance level of 0.05, i.e., there was only a 5% chance of incorrectly rejecting the hypothesis of equality of arithmetic means of pyrolysis yields when the two different methods of reactor inertization were applied. The explanation of this behavior is attributed to the temperature change inside the reactor, which shows that this particular fixed bed reactor suffers from local overheating in its middle part. Local overheating of the middle part of the reactor is more pronounced in the case of inerting the reactor from the bottom, which leads to greater excessive cracking of volatile products compared to the mode of inerting the reactor from the top part and thus greater formation of non-condensable gases, i.e., a reduction in the yield of pyrolytic oil. Full article

This study presents an automated control system for wastewater treatment, developed using machine learning (ML) models integrated into a Supervisory Control and Data Acquisition (SCADA) framework. The experimental setup focused on a laboratory-scale Aerobic Granular Sludge (AGS) reactor, which utilized synthetic wastewater to model real-world conditions. The machine learning models, specifically N-BEATS and Temporal Fusion Transformers (TFTs), were trained to predict Biological Oxygen Demand (BOD₅) values using historical data and real-time influent contaminant concentrations obtained from online sensors. This predictive approach proved essential due to the absence of direct online BOD₅ measurements and an inconsistent relationship between BOD₅ and Chemical Oxygen Demand (COD), with a correlation of approximately 0.4. Evaluation results showed that the N-BEATS model demonstrated the highest accuracy, achieving a Mean Absolute Error (MAE) of 0.988 and an R² of 0.901. The integration of the N-BEATS model into the SCADA system enabled precise, real-time adjustments to reactor parameters, including sludge dose and aeration intensity, leading to significant improvements in granulation stability. The system effectively reduced the standard deviation of organic load fluctuations by 2.6 times, from 0.024 to 0.006, thereby stabilizing the granulation process within the AGS reactor. Residual analysis suggested a minor bias, likely due to the limited number of features in the model, indicating potential improvements through additional data inputs. This research demonstrates the value of machine learning-driven predictive control for wastewater treatment, offering a resilient solution for dynamic environments. By facilitating proactive management, this approach supports the scalability of wastewater treatment technologies while enhancing treatment efficiency and operational sustainability. Full article

An experimental investigation into the conversion of ethanol to syngas by partial oxidation in a non-premixed counterflow moving bed filtration combustion reactor was carried out. Regimes of conversion depending on the mass flow rates of fuel and air (separate feeding), as well as a granular solid heat carrier, were studied. Depending on the mass flow rate of the heat carrier, two combustion modes were realized—reaction trailing and intermediate—with different temperature patterns in the gas preheating, combustion, and cooling zones along the reactor. The product gas composition is far from the predictions of the equilibrium model; it contains substation fractions of methane and ethylene. Combustion temperature and conversion are limited by the relatively high level of heat loss from the laboratory-scale reactor. The effect of the heat loss can be reduced by enhancing the absolute flow rate of the reactants. Full article

The implementation of inductive compensation in the two-scale models of the extra-high-voltage transmission line, without and with transposition, of the Physical Laboratory of Electric Power Systems (PLEPS) is presented. As they have voltages above the normal operating range, for a situation like that of the real 500 kV Coca Codo Sinclair–El Inga lines in Ecuador, the problem was solved by incorporating parallel inductive reactors located at the ends of the lines. The fixed bus compensation used was carried out by means of three-phase inductors in star connection with neutral-to-ground voltage and built-in iron cores with pitches of 50, 75 and 100% of the design capacity. Full article

Geothermal energy generation faces challenges in efficiency, partly due to restrictions on reinjection temperatures caused by scaling issues. Therefore, developing strategies to prevent scaling is critical. This study aims to simulate the scaling tendencies and corrosion effects of geothermal fluids on various construction materials used in scaling reactor/retention tank systems. A range of materials, including carbon steel, austenitic stainless steel, duplex stainless steel, two proprietary two-part epoxy coatings, and thermally sprayed aluminium (TSA), were tested in a simulated geothermal brine. Experiments were conducted in a laboratory vessel designed to replicate the wall shear stress conditions expected in a scaling reactor. The tests revealed varying scaling tendencies among the materials, with minimal corrosion observed. The dominant scale formed was calcium carbonate, consistent with geochemical modelling. The findings suggest that despite the high operating temperatures, the risk of corrosion remains low due to the brine’s low chloride content, while the wettability of materials after immersion may serve as a useful indicator for selecting those that promote scaling. Full article

With advancements in hardware and software, non-destructive radiometric analytical methods have become popular in a wide range of applications. A typical case is the study of the leaching process of metals from mineral ores and mine tailings. The objective of the current study was to develop a radiometric method based on neutron activation analysis (NAA), in particular, delayed gamma neutron activation analysis (DGNAA), to monitor the process of Mn and Zn leaching from Ti ore, Cu mine tailings, and Zn-Pb mine tailings. The DGNAA method was performed using a neutron source: a deuterium-tritium (D-T) neutron generator for Mn and a MARIA research nuclear reactor for Zn. Laboratory-scale Mn leaching from Ti ores, Cu tailings, and Zn-Pb tailings was investigated using delayed gamma-rays of ⁵⁶Mn (half-life of 2.6 h). The dissolution efficiencies of Mn were found to increase with interaction time and HCl concentration (1 to 5 M) and to vary with the leaching temperature (22.5 to 110 °C). Such results were found to agree with those obtained by total reflection X-ray fluorescence (TXRF) spectrometry for the same samples. ⁶⁵Zn (half-life of 244 days) was chosen to investigate real-time/online leaching of Zn in Ti ore, Cu tailings, and Zn-Pb tailings. During online monitoring, Zn recovery was also reported to increase with increased leaching time. After approximately 300 min of leaching, 80%, 79%, and 53% recovery of Zn in Zn-Pb tailings, Ti ore, and Cu tailings, respectively, were reported. Theoretically, developed mathematical prediction models for ⁶⁵Zn radiotracer analysis showed that the spherical diffusion model requires much less time to attain saturation compared to the linear diffusion model. The results of NAA for Zn were compared with those obtained by handheld X-ray fluorescence (handheld-XRF) and TXRF analysis. The analyzed samples encompassed leached Ti ore, Cu tailings, and Zn-Pb tailings which were subjected to different conditions of leaching time, temperature, and HCl concentrations. The XRF analysis confirmed that the leaching efficiencies of Zn rise with the increase in leaching time and HCl concentration and fluctuate with leaching temperature. The developed approach is important and can be applied in laboratories and industrial setups for online monitoring of the recovery of any element whose isotopes can be activated using neutrons. The efficiency of the metal-recovery process has a direct impact on the normal operation and economic advantages of hydrometallurgy. Full article