Anaerobic digestion: A review on process monitoring

Wu, Di; Li, Lei; Zhao, Xiaofei; Peng, Yun; Yang, Pingjin; Peng, Xiangjun

doi:10.1016/j.rser.2018.12.039

Cited by 165 publications

(71 citation statements)

References 85 publications

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“…Many of these materials are by-products of agricultural yield or municipal waste management, frequently classified as waste which has to be discarded. However, any improper disposal practices with regard to organic waste such as food waste (FW), will result in serious environmental problems as it contains a high moisture content, volatile solids, and salinity [5]. Furthermore, there are several techniques used worldwide to exploit this kind of waste in terms of converting it into a value-added product in the form of compost (biofertilizer), biogas, animal feed, and chemicals [6].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Food Waste and Biochar In-Vessel Co-Composting

et al. 2020

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As bulking agents (BA) affect the composting process, this work examined the impact of combinations of different organic components in order to obtain an efficient co-substrate for food waste (FW) in-vessel composting. To boost the occurrence of microorganisms inhabiting the compost, mature compost was firstly coupled with wheat straw, added to FW, and considered as a control (BC0). Then, two trials (BC10, BC20) including 10% and 20% of biochar were monitored. The results indicated that the temperature of the amended bioreactors was notably increased compared to the unamended one. Thermophilic temperatures were achieved at 14, 34, and 78 h after the experimental setup for BC20, BC10, and BC0, which lasted for 14, 17, and 12 days, respectively. When it came to an assessment of maturity and stability, the quality of the compost was evaluated against several indicators and compared with the compost quality standards of the UK, France, Canada, the USA, Poland, and Germany. BC10 illustrated a high-quality product in relation to the heavy metal concentration, a C:N ratio which reached 14.97, an AT4 which was lower than 6 (4.36 mg O2/g TS), and a nitrification index of 2.61 (<3). Consequently, the addition of 10% of biochar as a co-substrate showed an improvement of the process evolution and the characteristics of the biofertilizer produced.

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Section: Introductionmentioning

confidence: 99%

Optimization of Food Waste and Biochar In-Vessel Co-Composting

et al. 2020

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“…Additional criteria should also be considered at the industrial scale such as low cost and low maintenance. For example, Jimenez et al (2015) and Wu et al (2019) presented comprehensive overviews of different monitoring techniques used in AD processes. Moreover, a comprehensive overview of real-time monitoring techniques to measure sulfide, electron donors, sulfate, and biomass composition can be found in Cassidy et al (2015).…”

Section: On-line Monitoring 931 Sensors For Sulfurous Compoundsmentioning

confidence: 99%

“…Different techniques are available for measuring the composition of the biogas including H 2 S, such as Infrared (IR), Fourier transform infrared (FTIR), non-dispersive infrared (NDIR), ultraviolet (UV) and ultraviolet-visible (UV/VIS) photometers; diode laser sensing, specific analyzers based on electrochemical cells, thermal conductivity analyzers, and moisture sensors (Jimenez et al, 2015;Redondo et al, 2008;Wu et al, 2019). From these on-line measurements, a soft sensor (also known as a virtual sensor) based on a theoretical evaluation from the Henry's law and the sulfide dissociation equilibrium can be used to calculate the concentration of dissolved compounds.…”

Section: On-line Monitoring 931 Sensors For Sulfurous Compoundsmentioning

confidence: 99%

Anaerobic treatment of sulfate-rich wastewaters: process modeling and control

Steyer¹,

Bernet²,

Lens³

et al. 2020

Environmental Technologies to Treat Sulphur Pollution: Principles and Engineering

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A variety of anthropogenic activities, especially at industrial level, generate wastewater streams rich in sulfate which, if improperly treated, can cause health, social and environmental problems (Serrano et al., 2020). These industrial activities include food production (e.g. edible oil, seafood-processing), fermentation industry, tanneries, coal-burning power plants, paper and pulp industry, mining and metallurgical processes. Additionally, in some regions (e.g. Hong Kong), the use of seawater for toilet flushing to alleviate the pressure on potable water has increased the sulfate concentration in municipal sewage (Li et al., 2018; Wu et al., 2016). Anaerobic digestion (AD) is a well-established technology to transform municipal and industrial organic-rich wastewaters into renewable energy in the form of methane-rich biogas. Besides energy recovery, AD presents other important advantages compared to aerobic treatment such as low sludge production and low energy requirements (Puyol et al., 2017). AD is also a suitable technology to treat organic-rich municipal and industrial wastewaters containing sulfate. However, a high sulfate content can affect the AD process performance and feasibility due to (i) the competition for easily biodegradable organic matter (e.g. short chain fatty acids, hydrogen) between sulfate reducers and anaerobic microorganisms (Cassidy et al., 2015; Kalyuzhnyi et al., 1998), (ii) the generation of H 2 S, the product of sulfate

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“…One of the most frequently used forms of biological stabilization of sludge is anaerobic fermentation with simultaneous biogas recovery. This method gained popularity due to the fact that the energy generated during the process is regarded as a renewable one [16]. The sludge after anaerobic fermentation can still exhibit a tendency to release chemical substances, including malodorous compounds [17].…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

2019

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This paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in the fermentation sludge. The analyses revealed that the implementation of artificial neural networks allowed the prediction of the values of odor intensity and the hedonic tone of the post-fermentation sludge at the level of 30% mean absolute percentage error. Application of the decision tree made it possible to determine what input parameters the fermentation feed should have in order to arrive at the post-fermentation sludge with an odor intensity <2 and hedonic tone >−1. It was shown that the aforementioned phenomenon was influenced by the following factors: concentration of p-xylene, pH, concentration of volatile fatty acids, and concentration of p-cresol.

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Anaerobic digestion: A review on process monitoring

Cited by 165 publications

References 85 publications

Optimization of Food Waste and Biochar In-Vessel Co-Composting

Optimization of Food Waste and Biochar In-Vessel Co-Composting

Anaerobic treatment of sulfate-rich wastewaters: process modeling and control

The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process

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