Comparison between Thermo-Alkaline and Electro-Fenton Disintegration Effect on Waste Activated Sludge Anaerobic Digestion

Disintegration of municipal waste activated sludge (WAS) using thermo-alkaline (TA) and electro-Fenton (EF) methods was investigated and compared in terms of the efficiency of sludge solubilisation and enhancement of anaerobic biodegradability. Performance of organic matter solubilisation (soluble COD, proteins, polysaccharides) of sludge pretreated with EF was proved to be better than that with TA pretreatment, which resulted in the enhancement of anaerobic biodegradability. Comparison of results indicated that percentages of PN and PS release obtained after EF pretreatment (68.95 and 65.22%) were higher than those obtained by TA method (45.25 and 35.22%) respectively. An improvement of biogas potential about 2 and 1.6 times was achieved respectively by EF and TA pretreatment in comparison to raw sludge. During semi-continuous fermentation study in continuous stirred tank reactor, EF pretreated sludge gave the best biogas yield (0.6 L biogas/g COD) at an OLR of 2.5 g COD/L. d in comparison to TA pretreated sludge (0.3 L biogas/g COD), where low biogas yield about 0.1 L biogas/g COD was registered by raw sludge in the same CSTR. Therefore, the integration of EF process to anaerobic digestion might be a promising process for sludge reduction and biogas recovery.Scopu

Anaerobic Digestion of Olive MillWastewater and Process Derivatives—Biomethane Potential, Operation of a Continuous Fixed Bed Digester, and Germination Index

Olive mill wastewater (OMW) management is an economic and environmental challenge for olive oil-producing countries. The recovery of components with high added value, such as antioxidants, is a highly researched approach that could help refinance performant wastewater treatment systems. Anaerobic (co-)digestion is a suitable process to valorize the energetic and nutritional content of OMW and OMW-derived waste streams from resource recovery processes. Issues of process stability, operation, and yields discourage industrial application. Deepening the understanding of biomethane potential, continuous anaerobic digester operational parameters, and co-substrates is key to large-scale implementation. The biomethane potential of different OMWderived samples and organic solid market waste as co-substrate was 106–350 NL methane per kg volatile solids (VS). The highest yields were obtained with the co-substrate and depolyphenolized OMW mixed with retentate from an ultrafiltration pretreatment. Over 150 days, an anaerobic fixed-bed 300 L digester was operated with different OMW-derived substrates, including OMW with selectively reduced polyphenol concentrations. Different combinations of organic loading rate and hydraulic retention time were set. The biogas yields ranged from 0.97 to 0.99 L of biogas per g of volatile solids (VS) eliminated, with an average methane content in the produced biogas of 64%. Potential inhibition of the process due to high polyphenol concentrations or over-acidification through volatile fatty acids was avoided in the continuous process through process and substrate manipulation.The InnoVa research project (2nd German-African Innovation Promotion Prize: Prof. Sami Sayadi, Prof. Sven Geißen) was funded by the Bundesministerium für Bildung und Forschung, grant number 01DG20005, and managed by the Deutsche Luft-und Raumfahrtzentrum—Projektträger. This research was supported by the Ministry of Higher Education and Scientific Research-Tunisia under a contract program for the Laboratory of Environmental Bioprocesses (LR01CBS2015). We acknowledge support by the German Research Foundation and the Open Access Publication Fund of TU Berlin

Coupling air stripping process and anaerobic digestion for the treatment of landfill leachate: organics degradation and cytotoxicity evaluation

Landfill leachate (LFL) from a controlled discharge in Tunisia was found to be highly loaded with ammonia. This study investigated the feasibility of an air stripping process aiming to mitigate the inhibitory effect of ammonia. Optimization of this process, using an orthogonal central composite design, intended to reduce the ammonia concentration and also to fix it in a safe range for the subsequent anaerobic process. Optimization showed that, to remove 60% of ammonia, pH, air flow rate, and reaction time should be fixed at 10.8, 6 L min−1, and 18 h, respectively. The air stripping process improved the anaerobic digestion (AD) of leachate in an upflow anaerobic fixed bed reactor. Chemical oxygen demand (COD) removal efficiency exceeded 80% at an organic loading rate (OLR) of 1.3 g COD L−1 day−1. Analysis of the organic compounds monitored by gas chromatography coupled to mass spectrometry (GC–MS) showed that contaminants were efficiently removed after the anaerobic process. Cytotoxicity was significantly reduced subsequent to the air stripping and anaerobic process

Hybrid deep learning and HOF for Anomaly Detection

International audienceAnomalies detection in video footage is a daunting task treated with many challenges in crowded scenes. In this paper, we propose an efficient method based on deep learning and handcrafted spatio-temporal feature extraction for anomaly detection using a pre-trained CNN (convolution neural network) and HOF (Histogram of Optical Flow) features. Abnormal motion is picked by relative thresholding. One-class SVM is trained with spatial features for robust classification of abnormal shapes. Moreover, a decision function is applied to correct the false alarms and the miss detections. Our method has a high performance in terms of speed and accuracy. It achieved anomaly detection with good efficiency in challenging datasets and reduced computational complexity compared to state-of-the-art methods

Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos

International audienceIn the context of abnormal event detection in videos, only the normal events are available for the learning process, therefore the implementation of unsupervised learning method becomes paramount. We propose to use a new architecture denoted Two-Stream Fully Convolutional Networks (TS-FCNs) to extract robust representations able to describe the shapes and movements that can occur in a monitored scene. The learned FCNs are obtained by training two Convolutional Auto-Encoders (CAEs) and extracting the encoder part of each of them. The first CAE is trained with sequences of consecutive frames to extract spatio-temporal features. The second is learned to reconstruct optical flow images from the original images, which provides a better description of the movement. We enhance our (TS-FCN) with a Gaussian classifier in order to detect abnormal spatio-temporal events that could present a security risk. Experimental results on challenging dataset USCD Ped2 shows the effectiveness of the proposed method compared to the state-of-the-art in abnormal events detection

Two-streams Fully Convolutional Networks for Abnormal Event Detection in Videos

International audienceIn the context of abnormal event detection in videos, only the normal events are available for the learning process, therefore the implementation of unsupervised learning method becomes paramount. We propose to use a new architecture denoted Two-Stream Fully Convolutional Networks (TS-FCNs) to extract robust representations able to describe the shapes and movements that can occur in a monitored scene. The learned FCNs are obtained by training two Convolutional Auto-Encoders (CAEs) and extracting the encoder part of each of them. The first CAE is trained with sequences of consecutive frames to extract spatio-temporal features. The second is learned to reconstruct optical flow images from the original images, which provides a better description of the movement. We enhance our (TS-FCN) with a Gaussian classifier in order to detect abnormal spatio-temporal events that could present a security risk. Experimental results on challenging dataset USCD Ped2 shows the effectiveness of the proposed method compared to the state-of-the-art in abnormal events detection

Effect of bacterial lipase on anaerobic co-digestion of slaughterhouse wastewater and grease in batch condition and continuous fixed-bed reactor

Abstract Background This study aimed to investigate the effects of bacterial lipase on biogas production of anaerobic co-digestion of slaughterhouse wastewater (SHWW) and hydrolyzed grease (HG). A neutrophilic Staphylococcus xylosus strain exhibiting lipolytic activity was used to perform microbial hydrolysis pretreatment of poultry slaughterhouse lipid rich waste. Results Optimum proportion of hydrolyzed grease was evaluated by determining biochemical methane potential. A high biogas production was observed in batch containing a mixture of slaughterhouse composed of 75% SHWW and 25% hydrolyzed grease leading to a biogas yield of 0.6 L/g COD introduced. Fixed bed reactor (FBR) results confirmed that the proportion of 25% of hydrolyzed grease gives the optimum condition for the digester performance. Biogas production was significantly high until an organic loading rate (OLR) of 2 g COD/L. d. Conclusion This study indicates that the use of biological pre-treatment and FBR for the co-digestion of SHWW and hydrolyzed grease is feasible and effective

Hybrid deep learning and HOF for Anomaly Detection

International audienceAnomalies detection in video footage is a daunting task treated with many challenges in crowded scenes. In this paper, we propose an efficient method based on deep learning and handcrafted spatio-temporal feature extraction for anomaly detection using a pre-trained CNN (convolution neural network) and HOF (Histogram of Optical Flow) features. Abnormal motion is picked by relative thresholding. One-class SVM is trained with spatial features for robust classification of abnormal shapes. Moreover, a decision function is applied to correct the false alarms and the miss detections. Our method has a high performance in terms of speed and accuracy. It achieved anomaly detection with good efficiency in challenging datasets and reduced computational complexity compared to state-of-the-art methods

Analysis of LoRaWAN 1.0 and 1.1 Protocols Security Mechanisms

LoRaWAN is a low power wide area network (LPWAN) technology protocol introduced by the LoRa Alliance in 2015. It was designed for its namesake features: long range, low power, low data rate, and wide area networks. Over the years, several proposals on protocol specifications have addressed various challenges in LoRaWAN, focusing on its architecture and security issues. All of these specifications must coexist, giving rise to the compatibility issues impacting the sustainability of this technology. This paper studies the compatibility issues in LoRaWAN protocols. First, we detail the different protocol specifications already disclosed by the LoRa Alliance in two major versions, v1.0 and v1.1. This is done through presenting two scenarios where we discuss the communication and security mechanisms. In the first scenario, we describe how an end node (ED) and network server (NS) implementing LoRaWAN v1.0 generate session security keys and exchange messages for v1.0. In the second scenario, we describe how an ED v1.1 and an NS v1.1 communicate after generating security session keys. Next, we highlight the compatibility issues between the components implementing the two different LoRaWAN Specifications (mainly v1.0 and v1.1). Next, we present two new scenarios (scenarios 3 and 4) interchanging the ED and NS versions. In scenario three, we detail how an ED implementing LoRaWAN v1.1 communicates with an NS v1.0. Conversely, in scenario four, we explain how an ED v1.0 and an NS v1.1 communicate. In all these four scenarios, we highlight the concerns with security mechanism: show security session keys are generated and how integrity and confidentiality are guaranteed in LoRaWAN. At the end, we present a comparative table of these four compatibility scenarios