Processes

The conversion of renewable energy into hydrogen (H₂) by power-to-gas technologies involving electrolysis is seen today as a key element in the transition to a sustainable energy sector. Wastewater treatment plants (WWTP) could be integrated into future green H₂ networks as users of oxygen (O₂) produced alongside H₂ in water electrolysis. In WWTPs, O₂ is required for biological treatment steps, e.g., in activated sludge (AS) systems. However, the production costs of electrolysis O₂ should be competitive with those of conventional O₂ production processes. In this study, mathematical models of a polymer electrolyte membrane electrolyser (PEME) plant and the WWTP of the Benchmark Simulation Model No. 2 (BSM2) were used to simulate electrolysis O₂ supply to an AS system and estimate net costs of production (NCP) for produced O₂ via a techno-economic assessment (TEA). Assuming that produced H₂ is sold to a nearby industry, NCPs for O₂ were calculated for two different PEME plant dimensions, four alternatives regarding electricity supply and costs, and three sets of assumptions regarding system performance and market conditions. The analyses were performed for 2020 as a reference year and 2030 based on forecasts of relevant data. Results of the dimensioning of the PEME show the O₂ demand of a municipal WWTP with an installed capacity of 80,000 population equivalents (PE), such as the one of the BSM2, can be covered for more than 99% of the simulated period by either a 6.4 MW PEME operated for 4073 full load hours or a 4.8 MW PEME operated for 6259 full load hours. Investment costs for the PEME stacks and the operational costs for electricity make up most of the NCP of electrolysis O₂. The projected decrease in PEME stack costs and renewable energy prices in favourable market conditions can result in a competitive NCP for electrolysis O₂ in 2030. The approach described in this study can be applied to analyse O₂ supply to biological wastewater treatment in WWTPs with different characteristics, in processes different from AS, and under different assumptions regarding economic conditions. Full article

Magnetic CuFe₂O₄ powder obtained by sol−gel method and coupled photocatalysis was used to activate peroxydisulfate for tetracycline (TC) removal. A scanning electron microscope, X−ray diffraction Raman spectroscopy and FT−TR were used to characterize the catalysts. The degradation efficiency and stability of TC were highest under neutral conditions. The TC degradation rate reached 91.1% within 90 min. The removal rate of total organic carbon reaches 39.6% under optimal conditions. The unique electron transfer property of CuFe₂O₄ was utilized to achieve the synergistic effect of photocatalysis and persulfate oxidation. The main oxidizing substances involved in the decomposition were sulfate radicals and hydroxyl radicals, and the removal rate of over 84% could be maintained after five cycles of experiments. Full article

The initial excitation of interface crack of large-size ultra-thin chips is one of the most complicated technical challenges. To address this issue, the reversible fracture characteristics of a silicon-based chip (chip size: 1.025 mm × 0.4 mm × 0.15 mm) adhesive layer interface was examined by scanning electron microscope (SEM) tests, and the characteristics of a cohesive zone model (CZM) unit were obtained through peel testing. The fitting curve of the elastic bilinear model was in high agreement with the experimental data, with a correlation coefficient of 0.98. The maximum energy release rate required for stripping was G_C = 10.3567 N/m. Subsequently, a cohesive mechanical model of large-size ultra-thin chip peeling was established, and the mechanical characteristics of crack initial excitation were analyzed. The findings revealed that the larger deflection peeling angle in the peeling process resulted in a smaller peeling force and energy release rate (ERR), which made the initial crack formation difficult. To mitigate this, a coupling control method of structure and force surface was proposed. In this method, through structural coupling, the change in chip deflection was greatly reduced through the surface coupling force, and the peeling angle was greatly improved. It changed the local stiffness of the laminated structure, made the action point of fracture force migrate from the center of the chip to near the edge of the chip, the peeling angle was increased, and the energy release rate was locally improved. Finally, combined with mechanical analysis and numerical simulation of the peeling process, the mechanical characteristics of peeling were analyzed in detail. The results indicated that during the initial crack germination process, the ERR of the peel interface is significantly increased, the maximum stress value borne by the chip is significantly reduced, and the peel safety and reliability are greatly improved. Full article

The control of a permanent magnet synchronous motor (PMSM) without a position sensor based on a sliding-mode observer (SMO) algorithm has a serious jitter problem in the process of motor phase tracking. A second-order adaptive sliding-mode observer algorithm was proposed, and the ideas and principles of the second-order sliding-mode observer algorithm based on the super-twisting algorithm were elaborated. In particular, adaptive estimation with the introduction of back-electromotive force (EMF) was investigated, and the Lyapunov stability criterion was used to determine the convergence properties of the algorithm. The results showed that the second-order adaptive sliding-mode observer algorithm had better jitter suppression and a better phase tracking performance than the traditional sliding-mode observer algorithm. The experimental results showed that when the motor velocity was 800 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.57 r/min and the position error was 0.018 rad, with accuracy improvements of 93.63% and 58.34%, respectively. When the motor velocity was 1000 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.94 r/min and the position error was 0.022 rad, with accuracy improvements of 90.55% and 55.10%, respectively. The jitter of the system was suppressed well, the curve of back-EMF was smoother, and the robustness of the system was high. Therefore, the second-order adaptive sliding-mode observer algorithm is more suitable for the position-sensorless control of a PMSM. Full article

In the era of digitalization, the process industry is one of the sectors most affected by the need for change. The adoption of IoT-based intelligent monitoring systems for the collection of real-time measurements of energy and other essential operational variables, on one hand, makes it possible to accumulate big data useful for the company management to monitor the stability of the production process over time, and on the other hand, helps to develop predictive models that enable more efficient work and production. The OTTORTO project stems from the need of the FARRIS company to adapt its production line to agriculture 4.0 policies, responding to the higher goals of digitization and technological transition imposed at the national and EU level. The objectives of the current study are (i) to present an “ad hoc” customized intelligent and multi-parameter monitoring system to derive real-time temperature and humidity measurements inside the company’s industrial drying kilns; and (ii) to show how it is possible to extract information from operational data and convert it into a decision support too and an effective knowledge medium to better understand the production process. Studying the correlations between temperature and humidity measurements showed that for most of the observation period, the system was thermodynamically quite stable in terms of major operational risks, such as humidity saturation inside the kilns causing condensation on the products to be dried. However, to remedy the occasional occurrence of such inefficiencies, implementing kilns with the introduction of forced air extraction systems could bring significant benefits in terms of improved energy-environmental performance. Full article

The simulated moving bed (SMB) is a well-established, fully continuous process for chromatographic separation of difficult tasks with overlapping peaks, but it is relatively complex. The 1-SMB, which uses only one column but includes residence time zones to preserve concentration profiles, is a simpler semi-continuous alternative. This work examines the possible design of these residence time zones. Simulation studies were conducted to investigate the dependence of process metrics, such as purity, yield, productivity, and eluent consumption, on fluid dynamics. No deterioration in purity was observed, and the other variables remained constant over a wide range of axial dispersion before decreasing sharply. Pilot-scale experiments were conducted with various devices, including coiled flow inverters, eluate recycling devices, packed columns, and tank arrangements, to validate possible apparatus implementations with fluid dynamic measurements. It was demonstrated that the 1-SMB offers similar performance to the 4-SMB, albeit with reduced yield and lower apparatus complexity. Full article

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gases, especially existing buildings that do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework, including a multi-objective optimization model, and a ranking method are proposed to help decision-makers select the optimal energy efficiency measures. The multi-objective optimization model considers the economic and environmental objectives, expressed as the retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method, an evaluation and ranking method that combines the entropy weight method and ideal point method, is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework was implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can quickly identify near-optimal energy efficiency measures through multi-objective optimization and can select suitable energy efficiency measures using the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building was 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction in building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption was 59.84 kWh/m2, while the lowest value was 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures. Full article

Thermal energy recovery systems have different candidates to mitigate CO₂ emissions as recommended by the UN in its list of SDGs. One of these promising systems is thermal absorption transformers, which generally use lithium-water bromide as the working fluid. A Double Stage Heat Transformer (DSHT) is a thermal machine that allows the recovery of thermal energy at a higher temperature than it is supplied through the effect of steam absorption in a concentrated solution of lithium bromide. There are very precise thermodynamic models which allow us to calculate all the possible operating conditions of the DSHT. To perform the control of these systems, the use of Artificial Intelligence (AI) is proposed with two computational techniques—Fuzzy Logic (FL) and Artificial Neural Network (ANN)—to calculate in real-time the set of variables that maximize the product’s Gross Temperature Lift (GTL) and Coefficient of Performance (COP) in a DSHT. The values for Coefficient of Determination (R²), Mean Square Error Root (MRSE), and Mean Error Bias (MBE) for the two types of computational techniques were analyzed and compared with the purpose of identifying which of them may be more accurate to calculate the operating conditions (temperatures, pressures, concentration and flows) with the highest COP for an interval of the value of the temperature absorption entered by the user. The result of the analysis of the evaluated techniques concluded that the control strategy of a DSHT in real-time will be based on the precise calculation of the refrigerant flow in the second evaporator with a Neural Network of 30 neurons, 300 weights and 40 bias, as it is more accurate than the Fuzzy Logic technique. The goodness-of-fit for two computational techniques was evaluated as having an R² higher than 0.98 for the provided data. Future AI controllers must be based on evaporator flow values with evaporator power at 3.9⁻⁰⁴ kg/KJ. Full article

Understanding the evolution of pore-fracture networks in coal during loading is of paramount importance for coalbed methane exploration. To shed light on these dynamic changes, this study undertook uniaxial compression experiments on coal samples collected from the eastern edge of the Ordos Basin, complemented by μ-CT scanning to obtain a 3D visualization of the crack network model. The compression process was divided into three stages, namely, micro-crack compaction, linear elasticity, and peak failure. An increase in stress resulted in greater concentration and unevenness in fractal dimensions, illustrating the propagation of initial cleats and micro-cracks in the dominant crack direction and the ensuing process of crack merging. These results provide valuable insights into the internal structure and behavior of coal under stress, informing more efficient strategies for coalbed methane extraction. Full article

To investigate the dynamic thermal resistance of woven fabrics in different wetting states, ten commonly used clothing fabrics were selected and tested for fabric thermal resistance under different levels of water saturation in accordance with Chinese national standards. Based on Mangat’s eight thermal resistance prediction models, the study improved the models by replacing the original moisture content with water content saturation. The suitability of the eight models in predicting the thermal resistance of woven fabrics in wet states was compared using the sum of squared deviations (SSD), sum of absolute deviations (SAD), and correlation coefficient (R²). The results showed that during the process from initial wetting to complete immersion, the measured thermal resistance values of the ten fabric samples were consistent with the predicted values from Model 5 in the theoretical model of thermal resistance (R² > 0.955). The characteristic of Model 5 is that the air thermal resistance and water thermal resistance are first connected in parallel and then connected in series with the fiber thermal resistance. The corrected predicted values from Model 5 were highly consistent with the experimental measurement values and can be used to approximate the thermal resistance of woven fabrics in wet states. Full article

With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tested using large amounts of field data to predict the end-point steel temperature of basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl–Heraeus (RH) refining. The prediction model was applied to predict the degree of influence of different operating parameters on steel temperature. A comparison of the prediction results with the production data shows that the prediction system has good prediction accuracy, with a hit rate of over 90% for steel temperature deviations within 20 °C. Compared with the traditional steel temperature management model, the prediction system in this paper has higher management efficiency and a faster response time and is more practical and generalizable in the thermal management of steel. Full article

The Ladle Shroud has become an important part of secondary steelmaking, with its role in reducing liquid steel contamination and process improvements. Due to the inherent negative pressure at the lower nozzle–Ladle Shroud joint, it is well known that Ladle Shrouds, protecting steel flows between a Ladle and a tundish below, can suffer from inadvertent ingress of air. Therefore, there is a need to apply inert gas injection at the joint. In the present paper, 3D transient multi-phase simulations of flows occurring for a Reverse Tapered Ladle Shroud during start-up were studied using CFD software ANSYS Fluent 19.1. This allowed us to study the initial multi-phase flow developed during the start-up and potential steel reoxidation, based on a first principles approach. Time-dependent phase fields as well as attendant velocity and turbulence fields were obtained, resulting in the prediction of a turbulent multi-phase flow during start-up and filling. Additionally, some transient phenomena like steel splashing and air suction were observed mathematically. A full-scale water model of the Ladle Shroud was used to qualitatively validate the initial multi-phase turbulent flow inside the Ladle Shroud, in the absence of inert gas injection. Full article

The effects of ash and slag from a biomass power plant on the compressive strength, setting time and fluidity of the pastes of Portland cement (P.O) and sulfoaluminate cement (SAC) were studied, and the hydration products and microstructure at the age of 7 days were analyzed via XRD, SEM and other test methods. The results show that the compressive strength of the composite cementitious material decreases, the setting time prolongs and the fluidity increases with the increase in the ash and slag content in the power plant. The microscopic analysis shows that the ash and slag of the biomass power plant can promote the hydration of Portland cement and sulfoaluminate cement paste, and increase the generation of hydration products. The results showed that replacing SAC clinker with 20–30% biomass power plant ash (BPPA) decreased the cement strength, and that an appropriate amount of BPPA (10–15%) could significantly improve the mechanical strength of SAC blended cement. The compressive strength of blended BPPA composite cementitious material in 28 days could reach 60 MPa. This study provided solutions to utilizing the BPPA as a building material admixture to minimize the consumption of energy-intensive cement and to meet the growing needs of the construction industry. Full article

The production of titanium dioxide via the sulfuric acid process generates large amounts of acidic wastewater. Investigating the possibility of reusing this wastewater after deep treatment can reduce pollutant discharge and conserve water resources. In a pilot study, a dual-membrane method of ultrafiltration (UF) and reverse osmosis (RO) was employed to perform deep treatments of sulfuric-acid–titanium-dioxide wastewater. The findings showed that the multimedia and precision filters reduced the turbidity of water from an external drainage to as low as 0.18 NTU, with a turbidity removal rate of approximately 50%, reaching a maximum of 68%. When the UF effluent had a membrane flux of 70–100 L/m² h and a water production rate of 85–90%, the SDI15 was <5.0 and the turbidity was <1.0, meeting RO water supply requirements. Additionally, RO achieved a TDS removal rate of >95%, a COD_Cr removal rate of 85%, and a desalination rate of >98.5%. At a smooth operation system water recovery rate of 50%, the highest system recovery rate obtained was 64%. The water produced via RO adhered to reuse water standards. UF/RO deep treatment of sulfuric-acid–titanium-dioxide production wastewater and its reuse can realize comprehensive wastewater use and conserve water resources. Full article

Natural gas hydrate (NGH) is a kind of clean energy with great potential because of its huge reserves. There are several effective methods for exploiting hydrate sediments such as depressurization, thermal excitation, inhibitor injection and displacement, etc. Among these methods, the combined depressurization and heat injection method is considered a very promising method, which solves the problem of insufficient heat supply during the depressurization process. In this paper, the mechanism of combined depressurization and heat injection exploitation of NGH is analyzed, and the multiphase flow models of the injection well and production well are established, respectively, for the parallel horizontal NGH well production system with this combined method. The multiphase flow laws of fluids in a wellbore were obtained, and the factors affecting the temperature and pressure distributions in the wellbore were analyzed. The results of this study show that gas and water are produced simultaneously in the process of exploitation with this combined depressurization and heat injection method. The electric submersible pump has a great influence on the flow of the fluids in the wellbore, and there are sudden skips of the temperature and pressure at the pump position. Increasing the depth and working frequency of the pump will reduce the risk of continuous discharge of water from the annulus. Increasing the injection rate and injection temperature can both improve the effect of heat injection. This study provides theoretical guidance for the combined extraction with depressurization and heat injection method and production optimization of NGH. Full article

The landfill barriers effectively prevented the migration of high-concentration pollutants, such as NH₄⁺ and Na⁺, from the landfills to the surrounding environment. However, due to the high hydraulic head inside the landfill compared to the surrounding environment, NH₄⁺ and Na⁺ can migrate towards the outside of the landfill barrier with the infiltrating solution, potentially causing harm to the surrounding environment. To address this, saturated mixed soil column samples made of bentonite and Shanghai clay, with bentonite contents of 3% and 10%, were used in this study. Permeability coefficients of the column samples in solutions are obtained by using permeation tests, and using NaCl and NH₄Cl solutions with concentrations of 37.4 mmol/L and 74.8 mmol/L, respectively. The concentration-depth result of the column samples after permeation tests was determined using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Ion Chromatography (ICS-1100). Numerical simulations are used to investigate the effect of downstream solute concentration of the barriers on upstream solute concentration, dry density, and bentonite content of the barriers. The results indicate that the permeability coefficient of the soil column samples exposed to NH₄Cl solution is greater than that of samples exposed to NaCl solution. This can be attributed to the stronger cation exchange of montmorillonite for NH₄⁺, resulting in less swelling of the bentonite and more micro-pores, leading to an increase in the permeability coefficient. The concentration of Na⁺ is higher than that of NH₄⁺ at the same depth of the column samples, indicating that Na⁺ has a higher migration rate in the column sample. This could be attributed to the relatively fast diffusion of Na⁺ on the surface of the bentonite and larger hydration radius of Na⁺. According to the simulation results, the recommended values for the bentonite clay mixed-soil barrier wall are as follows in this study: a thickness of 43 cm, a dry density of 1.5 g/cm³, and a bentonite content of 5%. Full article

As societal interest in recycling of plastics increases, modeling thermochemical recycling of vinyl polymers, e.g., via pyrolysis or reactive extrusion, becomes increasingly important. A key aspect remains the reliability of the simulation results with fewer evaluation studies regarding convergence as in the polymerization or polymer reaction engineering field. Using the coupled matrix-based Monte Carlo (CMMC) framework, tracking the unzipping of individual chains according to a general intrinsic reaction scheme consisting of fission, β-scission, and termination, it is however illustrated that similar convergence demands as in polymerization benchmark studies can be employed, i.e., threshold values for the average relative error predictions on conversion and chain length averages can be maintained. For this illustration, three theoretical feedstocks are considered as generated from CMMC polymer synthesis simulations, allowing to study the effect of the initial chain length range and the number of defects on the convergence demands. It is shown that feedstocks with a broader chain length distribution and a long tail require a larger Monte Carlo simulation volume, and that the head–head effects play a key role in the type of degradation mechanism and overall degradation rate. A minimal number of chains around 5 × 10⁵ is needed to properly reflect the degradation kinetics. A certain degree of noise can be allowed at the higher carbon-based conversions due to the inevitable decrease in number of chains. Full article

A simple vapor-compression refrigeration system becomes ineffective and inefficient as it consumes a huge energy supply when operating between large temperature differences. Moreover, the recent Kigali amendment has raised a concern about phasing out some hydrofluorocarbon refrigerants due to their impact on the environment. In this paper, a numerical investigation is carried out to compare the performance of a cascade refrigeration system with two environmentally friendly refrigerant combinations, namely, R170–R404A and R41–R404A. Refrigerant R170, from the hydrocarbon category, and refrigerant R41, from the hydrofluorocarbon category, are separately chosen for the low-temperature circuit due to their similar thermophysical properties. On the other hand, refrigerant R404A is selected for the high-temperature circuit. An attempt is made to replace refrigerant R41 with refrigerant R170 as a possible alternative. The condenser temperature is kept constant at 40 °C, and the evaporator temperature is varied from −60 °C to −30 °C. The mathematical model developed for the cascade refrigeration system is solved using Engineering Equation Solver (EES). The effect of evaporator temperature on different performance parameters such as the COP, exergetic efficiency, and total plant cost rate is evaluated. The predicted results show that the thermoeconomic performance of the R170–R404A-based system is marginally lower compared to that of the R41–R404A-based system. The system using refrigerant pair R170–R404A has achieved only a 2.4% lower exergetic efficiency compared to the system using R41–R404A, with an increase in the annual plant cost rate of only USD 200. As the global warming potential (GWP) of R170 is less than that of R41, and R170 belongs to the hydrocarbon category, the use of the R170–R404A combination in a cascade refrigeration system can be recommended as an alternative to R41–R404A. Full article

The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. Non-invasive gamma attenuation systems are one of the most accurate detection methods. To accomplish this goal, the Monte Carlo N Particle (MCNP) algorithm was used to simulate a scale thickness measurement system, which included two sodium iodide detectors, a dual-energy gamma source (241 Am and 133 Ba radioisotopes), and a test pipe. Water, gas, and oil were all used to mimic a three-phase flow in the test pipe, with the volume percentages ranging from 10% to 80%. Moreover, a scale ranging in thickness from 0 to 3 cm was inserted into the pipe, gamma rays were shone on the pipe, and on the opposite side of the pipe, photon intensity was measured by detectors. There were 252 simulations run. Fifteen time and frequency characteristics were derived from the signals collected by the detectors. The ant colony optimisation (ACO)-based approach is used to pick the ideal inputs from among the extracted characteristics for determining the thickness of the scale within the pipe. This technique led to the introduction of thirteen features that represented the ideal combination. The features introduced by ACO were introduced as inputs to a multi-layer perceptron (MLP) neural network to predict the scale thickness inside the oil pipe in centimetres. The maximum error found in calculating scale thickness was 0.017 as RMSE, which is a minor error compared to earlier studies. The accuracy of the present study in detecting scale thickness has been greatly improved by using the ACO to choose the optimal features. Full article