Target Parameters Research Articles

Highly efficient crystallization for sustainable azeotrope separation of formic acid-Water

The cost-effective separation of azeotropes poses a shared challenge. In this work, we utilized a green technique: layer melt crystallization (LMC), to separate the formic acid and water. Characterized by the lower energy consumption and high selectivity, LMC is welcome in high-purity production preparation. Firstly, we discussed the effects of temperature, cooling and heat rates, and time on the separation performance. We discovered that temperatures were important in the solute distribution and impurity migration. Then, the stirring technique is coupled to optimize the separation performance. Under the optimal trajectory, the product purity exceeds 99.7%. Next, to better understand the crystallization process, several theoretical models are established to clarify the intrinsic correlations of process variables and target parameters. Kinetic models are established to clarify the crystal layer growth, and sweating melt flow with the temperature difference-based driving force. Interfacial solute distribution factor and mass transfer coefficient are introduced to give the solute distribution rule and impurity migration mechanisms. At last, a dimensionless number is used to evaluate the sweating intensity, and we also correlated this coefficient with the separation performance of LMC qualitatively. In conclusion, LMC provides a viable approach for separating azeotropes, and the proposed theoretical models can offer valuable support for crystallization design.

Experimental investigation and application of soft computing models for predicting flow energy loss in arc-shaped constrictions

Abstract This investigation focuses on flow energy, a crucial parameter in the design of water structures such as channels. The research endeavors to explore the relative energy loss (ΔEAB/EA) in a constricted flow path of varying widths, employing Support Vector Machine (SVM), Artificial Neural Network (ANN), Gene Expression Programming (GEP), Multiple Adaptive Regression Splines (MARS), M5 and Random Forest (RF) models. Experiments span a Froude number range from 2.85 to 8.85. The experimental findings indicate that the ΔEAB/EA exceeds that observed in a classical hydraulic jump with constriction section. Within the SVM model, the linear kernel emerges as the best predictor of ΔEAB/EA, outperforming polynomial, radial basis function (RBF), and sigmoid kernels. In addition, in the ANN model, the MLP network was more accurate compared to the RBF network. The results indicate that the relationship proposed by the MARS model can play a significant role resulting in high accuracy compared to the non-linear regression relationship in predicting the target parameter. Upon comprehensive evaluation, the ANN method emerges as the most promising among the candidates, yielding superior performance compared to the other models. The testing phase results for the ANN-MLP are noteworthy, with R = 0.997, average RE% = 0.63%, RMSE = 0.0069, BIAS = −0.0004, DR = 0.999, SI = 0.0098 and KGE = 0.995.

Scattering Field Intensity and Orbital Angular Momentum Spectral Distribution of Vortex Electromagnetic Beams Scattered by Electrically Large Targets Comprising Different Materials

In this study, we obtained the intensity and orbital angular momentum (OAM) spectral distribution of the scattering fields of vortex electromagnetic beams illuminating electrically large targets composed of different materials. We used the angular spectral decomposition method to decompose a vortex beam into plane waves in the spectral domain at different elevations and azimuths. We combined this method with the physical optics algorithm to calculate the scattering field distribution. The OAM spectra of the scattering field along different observation radii were analyzed using the spiral spectrum expansion method. The numerical results indicate that for beams with different parameters (such as polarization, topological charge, half-cone angle, and frequency) and targets with different characteristics (such as composition), the scattering field intensity distribution and OAM spectral characteristics varied considerably. When the beam parameters change, the results of scattering from different materials show similar changing trends. Compared with beams scattered by uncoated metal and dielectric targets, the scattering field of the coating target can better maintain the shape and OAM mode of beams from the incident field. The scattering characteristics of metal targets were the most sensitive to beam-parameter changes. The relationship between the beam parameters, target parameters, the scattering field intensity, and the OAM spectra of the scattering field was constructed, confirming that the spiral spectrum of the scattering field carries the target information. These findings can be used in remote sensing engineering to supplement existing radar imaging, laying the foundation for further identification of beam or target parameters.

Laying the experimental foundation for corrosion inhibitor discovery through machine learning

Creating durable, eco-friendly coatings for long-term corrosion protection requires innovative strategies to streamline design and development processes, conserve resources, and decrease maintenance costs. In this pursuit, machine learning emerges as a promising catalyst, despite the challenges presented by the scarcity of high-quality datasets in the field of corrosion inhibition research. To address this obstacle, we have created an extensive electrochemical library of around 80 inhibitor candidates. The electrochemical behaviour of inhibitor-exposed AA2024-T3 substrates was captured using linear polarisation resistance, electrochemical impedance spectroscopy, and potentiodynamic polarisation techniques at different exposure times to obtain the most comprehensive electrochemical picture of the corrosion inhibition over a 24-h period. The experimental results yield target parameters and additional input features that can be combined with computational descriptors to develop quantitative structure–property relationship (QSPR) models augmented by mechanistic input features.

Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing.

In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.

FlectoSol – A pneumatically activable PV-functionalized façade shading module with bending motion in two directions for solar tracking

A continuously adjustable façade shading system functionalized with photovoltaics enables, besides adaptive shading, energy harvesting by solar tracking. This requires large bending motion in two directions. In this paper, the development of an appropriate façade module – FlectoSol – is presented. To achieve motion of ±80°, first time two pneumatic actuators are integrated into a GFRP-elastomer hybrid composite. To improve energy efficiency resp. air pressure consumption of actuation without impairing shading, a parametric study is performed. In detail the influencing criteria of the bending motion “overlap of the actuators” resp. “stiffness ratio of the actuator-surrounding GFRP” and their effect on the target parameters “bending angle”, “shadow width” resp. “air pressure consumption” are analyzed. It could be stated that the “stiffness ratio” only effects the air pressure consumption, but the “overlap” also effects the shadow width. The bending angle itself is, up to ±80°, only limited by the absolute laminate stiffness.

Multi-objective optimization of emission parameters of a diesel engine using oxygenated fuel and pilot injection strategy based on RSM-NSGA III

In this work, a diesel engine fueled with PODE3/n-butanol/diesel blends at high altitudes was optimized using the RSM-NSGA III hybrid optimization methodology to achieve a reduction in emissions. The target parameters of the optimization were soot, NOx, and HC emissions, and the decision variables were the mass ratio of pilot injection (MRPI), pilot injection timing (PIT), mass ratio of PODE3 (MRP) and mass ratio of n-butanol (MRB). Firstly, a three-dimensional simulation model of the CI engine was built in CONVERGE, and the model was verified through experiments. Secondly, the effects of the pilot injection strategy, also known as the pre-injection strategy, on the combustion characteristics of the CI engine were analyzed. Then, an experimental design based on Box-Behnken Design (BBD) was conducted and a mathematical model of the target parameters was obtained using RSM. Finally, the mathematical model of RSM was optimized using NSGA III to obtain the Pareto frontier, and the optimal solution was selected from the Pareto frontier using TOPSIS. The results showed that a suitable pilot injection strategy helped alleviate the problem of low cylinder pressure (CP) and high soot and HC emissions from the CI engine at high altitudes. The soot and HC emissions exhibited a decreasing and then increasing trend as the PIT advanced. As MRPI increased, both soot and HC emissions decreased. The NOx emissions showed the opposite trend. Compared with the non-optimized results, at an altitude of 2000 m, the optimized engine exhibited a 65.29% decrease in soot emissions, a 24.7% increase in NOx emissions, and a 74.94% decrease in HC emissions. Compared with the non-optimized results, at an altitude of 4000 m, the optimized engine demonstrated a 54.70% decrease in soot emissions, a 15.71% increase in NOx emissions, and a 65.78% decrease in HC emissions. This research will contribute to the application of oxygenated fuels in plateau diesel engines and the optimization of plateau diesel engines.

Impact of model-informed precision dosing in adults receiving vancomycin via continuous infusion: a randomized, controlled clinical trial

Background Vancomycin is a commonly prescribed antibiotic to treat gram-positive infections. The efficacy of vancomycin is known to be directly related to the pharmacokinetic/pharmacodynamic (PK/PD) index of the area under the concentration-time curve (AUC) divided by the minimal inhibitory concentration (MIC) of the pathogen. However, in most countries, steady-state plasma concentrations are used as a surrogate parameter of target AUC/MIC, but this practice has some drawbacks. Hence, direct AUC-guided monitoring of vancomycin using model-informed precision dosing (MIPD) tools has been proposed for earlier attainment of target concentrations and reducing vancomycin-related nephrotoxicity. However, solid scientific evidence for these benefits in clinical practice is still lacking. This randomized controlled trial (RCT) aims to investigate the clinical utility of MIPD dosing of vancomycin administered via continuous infusion in hospitalized adults.Methods Participants from 11 wards at two Belgian hospitals are randomly allocated to the intervention group or the standard-of-care comparator group. In the intervention group, clinical pharmacists perform dose calculations using CE-labeled MIPD software and target an AUC24h of 400 to 600 mg × h/L, whereas patients in the comparator group receive standard-of-care dosing and monitoring according to the institutional guidelines. The primary endpoint is the proportion of patients reaching the target AUC24h/MIC of 400–600 between 48 and 72 h after start of vancomycin treatment. Secondary endpoints are the proportion of patients with (worsening) acute kidney injury (AKI) during and until 48 h after stop of vancomycin treatment, the proportion of patients reaching target AUC24h/MIC of 400–600 between 72 and 96 h after start of vancomycin treatment, and the proportion of time within the target AUC24h/MIC of 400–600.Discussion This trial will clarify the propagated benefits and provide new insights into how to optimally monitor vancomycin treatment.Trial registrationEudraCT number: 2021-003670-31. Registered June 28, 2021. ClinicalTrials.gov identifier: NCT05535075. Registered September 10, 2022. Protocol version 3, protocol date: April 21, 2023.

High-Precision Joint TDOA and FDOA Location System

Passive location based on TDOA (time difference of arrival) and FDOA (frequency difference of arrival) is the mainstream method for target localization. This paper proposes a fast time–frequency difference positioning method to address issues such as low accuracy, large computational resource utilization, and limited suitability for real-time signal processing in the conventional CAF (cross-ambiguity function)-based approach, aiming to complete the processing of the target radiation source to obtain the target parameters within a short timeframe. In the mixing product operation step of the CAF, a frequency-domain approach replaces the time-domain convolution operation in PW-ZFFT (pre-weighted Zoom-FFT) to reduce the computational load of the CAF. Additionally, a quadratic surface fitting method is used to enhance the accuracy of TDOA and FDOA. The localization solution is obtained using Newton’s method, which can provide more accurate results compared to analytical methods. Next, a signal processing platform is designed with FPGA (field-programmable gate array) and multi-core DSP (digital signal processor), and works by dividing and mapping the algorithm functional modules according to the hardware’s characteristics. We analyze the architectural advantages of multi-core DSP and design methods to improve program performance, such as EDMA transfer optimization, inline function optimization, and cache optimization. Finally, this paper constructs simulation tests in typical positioning scenarios and compares them to hardware measurement results, thus confirming the correctness and real-time capability of the program.

Structural and mechanical optimization of porous alumina structures fabricated by carbon sacrificial template

The Taguchi method is used to optimize the manufacture of porous alumina made through reactive spark plasma sintering and carbon sacrificial template. The goal is to design a new versatile procedure that allows the fabrication of porous alumina with tailored physical properties. The structural and mechanical properties taken as target parameters were the subtle combination of porosity and Young’s modulus of the human cortical bone: typical pore size ¿100μm, and Young’s modulus in the range of 3–30 GPa. The input factors of the Taguchi method are wt.% of carbon, sintering time, calcination heating rate, and final heat treatment. Hg porosimetry, electron microscopy, uniaxial compression and computer aided tomography were used for the characterization of the porosity, pore size distribution, pore interconnectivity, and Young’s modulus. Finally, according to the conclusions of the Taguchi analysis, the parameters of the process were changed for the fabrication of the new samples with optimized properties. Highly porous structures with 90% interconnectivity, Young modulus of 5.5 ± 1.1 GPa, and compression strength of 49 ± 20 MPa, were obtained, successfully emulating the targeted properties.

Experimental investigation on temperature-dependent effective thermal conductivity of ceramic fiber felt

This paper presents a method to invert the effective thermal conductivity of ceramic fiber felt under high temperatures using experimentally measured specific heat capacity, density, and transient temperature data. The method combines the solution of the heat transfer equation with an inversion method based on real-valued genetic algorithm (GA) optimization, and 11 target parameters can be simultaneously inverted with one experimental dataset. The effective thermal conductivities of ceramic fiber felt were obtained by inversion under ambient pressures of 2 Pa and 100 kPa, compression ratios of 0.8–1.0, and temperature ranges of 295∼1273 K. The results indicates that the effective thermal conductivities of ceramic fiber felt derived from the inversion method are in concurrence with the outcomes of the steady state method. The effective thermal conductivities of the ceramic fiber felt range from 0.069 to 0.321 W m−1 K−1. It is observed that the effective thermal conductivity increases with higher temperature, and decreases with growing compression ratio. The impact of ambient pressure on effective thermal conductivity is linked to the level of compression.

ALR_Sim_tracks — Trajectory Simulator Software to Assist the Search for Favourable Trajectories for the Exploration of the Triple Asteroid 2001-SN263 From the Laser Altimeter Point of View

This paper presents a simulator tool, named _ALR_Sim_tracks_, dedicated to the preliminary analysis of trajectories planned for a space mission to explore the surface of celestial bodies using optical sensors, such as a laser altimeter. The software offers a prediction of the coverage results obtainable in a simulated exploration campaign carried out along a selected/simulated trajectory, for the set of target and instrument parameters used (in qualitative and quantitative terms, including 2D/3D visualizations). The software was created to perform an analysis that would allow the identification of favourable trajectories for conducting the intended exploration of the triple asteroid 2001-SN263 during Brazil's first deep space mission (ASTER mission) from the point of view of the Laser Altimeter being designed to fly in this mission, which was named ALR. Because there are few tools described and available for this type of analysis in the literature, and these are generally complex, proprietary and of restricted use, this work aims to help broaden the understanding of the process involved and the access to this type of tool. The paper contains the description of the _ALR_Sim_tracks_ software and everything involved in its operation. The illustrative examples presented in this text involve the exploration of an asteroid from the point of view of a laser altimeter, because this was the initial motivation for creating the simulator. However, other optical instruments with similar operation, used to explore other types of targets (e.g. planets or moons), are also considered when suitable modelling is available.

The latest approaches and technologies to increase the competitiveness of aviation enterprises in modern conditions

The article discusses the essence of the concept of enterprise competitiveness management, encompassing all enterprise management processes, such as production, sale of products, provision of services, and others. It has been identified that the spectrum of existing research on scientific approaches to competitiveness management requires some structuring and refinement, taking into account the latest trends and the specifics of the activities of aviation enterprises in modern conditions. A unique perspective on structuring scientific approaches to managing the competitiveness of aviation enterprises has been proposed, according to which the discussed approaches are categorized depending on the object, target parameters, scientific directions, innovative technologies, and combined objects. The advantage of such structuring lies in a better understanding of the specifics of existing approaches and the identification of innovative scientific approaches to enterprise competitiveness management that require further research. Special attention has been paid to the study of key aspects and specifics of the logistic approach, which involves finding a compromise and balance of interests and responsibilities, consistency and coordination of actions, subordination of partial to general, and measuring the results of activities not only from the perspective of resource savings but also the magnitude of losses caused by irrational actions of chain participants. The logistic approach to managing the competitiveness of aviation enterprises lies in the implementation of the supply chain management concept, aimed at effectively servicing demand and creating additional value for consumers, and realizes a customer-oriented approach to competitiveness management. Direct tools of the logistic approach that can be effective in managing aviation logistic processes have been identified in the article, such as ESG technologies, CALS technologies, Agile approach, IoT, Blockchain, Artificial Intelligence, robotic systems. The advantages of these tools and their application areas are indicated. Thus, the use of various approaches and technologies will enable aviation enterprises to find optimal solutions to enhance their competitiveness in modern conditions.

Vision-oriented machine learning-assisted seismic energy dissipation estimation for damaged RC beam-column connections

After a seismic event, damage quantification is necessary for safety evaluation, collapse proximity judgment, and measurement of the residual seismic capacity of the joints. Machine learning-based predictive models are developed in this paper aimed at the non-destructive and non-contact estimation of energy dissipation ratio for seismically damaged beam-column joints through surface crack texture complexity indices. For this purpose, the authors collected an experimental dataset comprising 934 images of cracked and/or crushed reinforced concrete-framed joints from 254 specimens under cyclic loading. The associated energy dissipation ratios are then extracted from the lateral load-displacement hysteresis curves for each image by the authors, along with the image-derived fractal indices. Machine learning models with six algorithms, including Linear Regression, Multi-Layer Perceptron, Decision Tree, Random Forest, Gradient Boost, and CatBoost, are trained by 80% of images as a training dataset and validated through the remaining 20% of images in two scenarios. The first scenario develops models by component geometric dimensions and generalized fractal indices calculated for the beams, columns, and joints of seismically damaged connections, while the second scenario adds structural parameters to visual indices to estimate the energy dissipation ratio as the target parameter. Performance evaluation of trained models in the mentioned scenarios via common correlation and accuracy measures, along with the k-fold cross-validation method, indicated that CatBoost-based ML models in both scenarios provide optimal results with R2 = 0.89, RMSE = 0.10 for scenario I and R2 = 0.92, RMSE = 0.09 for scenario II using testing dataset. The first scenario based on the CatBoost algorithm with the R2 variation range of 0.07 and RMSE variation range of 0.03 across the test folds provides a sufficient performance in terms of generalizability and accuracy for estimating the energy dissipation ratio through lower number of input variables.

Deep brain stimulation for refractory major depressive disorder: a comprehensive review.

Deep brain stimulation (DBS) has emerged as a promising treatment for select patients with refractory major depressive disorder (MDD). The clinical effectiveness of DBS for MDD has been demonstrated in meta-analyses, open-label studies, and a few controlled studies. However, randomized controlled trials have yielded mixed outcomes, highlighting challenges that must be addressed prior to widespread adoption of DBS for MDD. These challenges include tracking MDD symptoms objectively to evaluate the clinical effectiveness of DBS with sensitivity and specificity, identifying the patient population that is most likely to benefit from DBS, selecting the optimal patient-specific surgical target and stimulation parameters, and understanding the mechanisms underpinning the therapeutic benefits of DBS in the context of MDD pathophysiology. In this review, we provide an overview of the latest clinical evidence of MDD DBS effectiveness and the recent technological advancements that could transform our understanding of MDD pathophysiology, improve the clinical outcomes for MDD DBS, and establish a path forward to develop more effective neuromodulation therapies to alleviate depressive symptoms.

A New Method for Defining the Optimal Separation Gap Distance and the Acceptable Structural Pounding Risk on Multistory RC Structures

A proposal to control the structural pounding hazard imposed on multistory reinforced concrete (RC) structures is presented. The main goal is to guarantee the seismic performance of a structure with an acceptable (predefined) risk-targeted parameter without the need to eliminate structural pounding collisions. The key target parameters of this study are the annual probability of exceeding an engineering demand parameter (EDP) capacity level and the separation distance dg between adjacent structures. In this direction, a method that ensures the performance level of critical EDPs due to structural pounding conditions is proposed. The new method involves two decision frameworks that define (a) the optimal separation gap distance dg,minPt at a targeted value of pounding risk (probability per year) Pt (Decision A) and (b) the minimum acceptable structural pounding risk Pmindg,t at a targeted value of separation gap distance dg,t (Decision B). The demand parameters that are incorporated in the proposed method are the peak relative displacement δmax at the top level of colliding without considering pounding conditions and any other critical EDP due to the structural pounding effect. The overall method is based on two distinct acceptable performance objectives, the POs-δmax and the POs-EDP, defined as a function of P vs. dg. For this purpose, a seismic hazard curve compatible with Eurocode’s 8 hazard zone is adopted, and the corresponding demand hazard curves of δmax and EDP are developed. The proposed method is implemented to study the floor-to-floor structural pounding hazard of an eight-story RC frame taking into account different risk-targeted scenarios. The results show that the seismic risk (probability per year) of exceeding the EDP’s capacity level is significantly increased due to structural pounding in comparison to the case of no pounding. Calibration of the structural pounding risk can be obtained by adjusting the separation gap distance dg between the adjacent structures based on the acceptable POs. The POs-δmax is not always an accurate criterion for verifying the capacity level of the critical EDP. Finally, with the proposed method, a variety of POs-EDPs can be used to control the structural pounding risk in terms of dg,minPt and/or Pmindg,t.

An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar.

Highly efficient freezing desalination technology: Parameters optimization and model configuration

Seawater desalination has become an available route to reduce the shortage of freshwater resources. Lower energy consumption and suitable operational conditions make freezing crystallization a green technique in the desalination area. Herein, we coupled the sweating intensification with the freezing crystallization to reach a highly efficient desalination performance. Under the trajectory of the freezing temperature and time: 259.15 K and 150 min, the sweating temperature, heating rate, and sweating time: 272.15 K, 0.5 K/min, and 120 min, the desalination ratio exceeds 70 %. Meanwhile, we established the theoretical models to expound the intrinsic correlations of process variables and target parameters, to visualize the impurity migration. Two kinetic models are proposed to analyze temperature effects on the crystal layer growth, sweating melt flow, and inclusion entrapment. Next, the fractal slice model is proposed to describe the complex microstructure of the crystal pillar. The thermal coefficients are deduced based on the fractal theory, where the larger porosity is against thermal conduction. Finally, we discussed the kinetic and porosity effects on the impurity migration and desalination performance, and established a boundary layer model to correlate the separation efficiency with process kinetic and impurity distribution successfully.

Why do metals become superhydrophilic during nanosecond laser processing? Design of superhydrophilic, anisotropic and biphilic surfaces

This research investigates the impact of the surrounding environment on the characteristics of copper, nickel and tin during nanosecond laser processing. By comparing the results of processes conducted in vacuum and air, we conclude that changes in wetting properties cannot be solely attributed to development of surface microstructure. To elucidate this phenomenon, we conducted model experiments using a tin target. Laser processing parameters for the tin target were established, involving deep cavity melting without ablation, resulting in highly evolved material morphology. Under these conditions, laser processing in air did not lead to metal hydrophilization. Therefore, our study demonstrates that the primary cause of changes in material wetting properties during nanosecond laser processing is the re-deposition of ablation products onto the material surface. These products form a nanoporous layer that enhances wicking capabilities and subsequently serves as a sorbent for various impurities, gradually leading to the hydrophobization of most commonly used materials. The proposed mechanism opens the possibility for the development of new methods to create materials with biphilic and anisotropic wetting properties. The key insight lies in the control of the thickness of the nanoporous layer, as it emerges as a pivotal factor dictating wettability properties and wicking capability.

Combining pretreatments and co-fermentation as successful approach to improve biohydrogen production from dairy cow manure

The present paper is focused on enhancing the production of biohydrogen (bioH2) from dairy cow manure (DCM) through dark fermentation (DF). Two enhancement production strategies have been tested: i) the combination of H2O2 with sonification as pretreatment and ii) the co-fermentation with cheese whey as co-substrate. Concerning the pretreatment, the best combination was investigated according to the response surface methodology (RSM) by varying H2O2 dosage between 0.0015 and 0.06 g/gTS and ultrasonic specific energy input (USEI) between 35.48 and 1419.36 J/gTS. The increase of carbohydrates concentration was used as target parameter. Results showed that the combination of 0.06 g/gTS of H2O2 with 1419.36 J/gTS of USEI maximized the concentration of carbohydrates. The optimized conditions were used to pretreat the substrate prior conducting DF tests. The use of pretreatment resulted in obtaining a cumulative bioH2 volume of 51.25 mL/L and enhanced the bioH2 production by 125% compared to the control test conducted using raw DCM. Moreover, the second strategy, i.e. co-fermentation with cheese whey (20% v/v) as co-substrate ended up to enhancing the DF performance as the bioH2 production reached a value of 334.90 mL/L with an increase of 1372% compared to the control DF test. To further improve the process, dark fermentation effluents (DFEs) were valorized via photo fermentation (PF), obtaining an additional hydrogen production aliquot.