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Organic Waste Treatment, Recycling, and Reuse
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Organic Waste Treatment, Recycling, and Reuse

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Resources and Sustainable Utilization".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 7287

Special Issue Editor

Dr. Xiaowei Li

E-Mail Website
Guest Editor
School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China
Interests: biological treatment and resource utilization of organic solid wastes; such as sewage sludge; food waste; rural human faeces; fate and potential risk of emerging pollutants such as microplastics; PPCPs during the resource utilization of organic solids wastes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Organic wastes such as human excreta, food waste, animal wastes, and sewage sludge contain a lot of pollutants, including labile organic matter, pathogenic bacteria, other materials, and emerging pollutants such as pharmaceuticals and personal care products, but they also contain valuable substances including carbon (C), nitrogen (N), phosphorus (P), and other trace elements. At present, many physical, chemical, and biological techniques and their combination have been used to treat, recover, and reuse organic wastes. The incineration and pyrolysis of organic wastes are examples of physical and chemical methods of energy recovery from municipal and agricultural solid wastes; however, these methods involve very high investment and operation costs. The treatment and recycling of organic wastes seems to be most effectively accomplished using biological processes, employing the activities of microorganisms such as bacteria, algae, fungi, and other higher life forms. However, the efficiency of biological treatments depends on environmental conditions (such as temperature), and application of by-products from these biological processes, including compost fertilizer, biofuels, and protein biomass, is not easy.  Therefore, simple, practical, and economical technologies of organic waste treatment, recycling, and reuse should be given priority.

The aims of this Special Issue are to draw attention to the sustainable development of organic waste treatment, recycling, and reuse, and to promote the exchange of research that focuses on organic waste management.

Specifically, the issue will cover (but is not limited to) the following topics:

  • The physical treatment, recovery, and reuse of organic wastes such as human excreta, food waste, animal wastes, and sewage sludge.
  • The chemical treatment, recovery, and reuse of organic wastes such as human excreta, food waste, animal wastes, and sewage sludge.
  • The biological treatment, recovery, and reuse of organic wastes such as human excreta, food waste, animal wastes, and sewage sludge.
  • The thermal treatment, recovery, and reuse of organic wastes such as human excreta, food waste, animal wastes, and sewage sludge.
  • The combination of organic wastes treatment and recycling.
  • The pretreatment of organic wastes such as human excreta, food waste, animal wastes, and sewage sludge.
  • By-product application of organic waste treatment such as compost fertilizer, biofuels, and protein biomass.
  • Other emerging technology regarding the treatment, recovery, and reuse of organic wastes.

I look forward to receiving your contributions.

Dr. Xiaowei Li
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • organic waste
  • sewage sludge
  • food waste
  • animal waste
  • physical treatment
  • chemical treatment
  • biological treatment
  • thermal treatment
  • recycling
  • reuse
  • resource utilization

Published Papers (3 papers)

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Research

16 pages, 1413 KiB  
Article
Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management
by Mohammed Imran Basheer Ahmed, Raghad B. Alotaibi, Rahaf A. Al-Qahtani, Rahaf S. Al-Qahtani, Sara S. Al-Hetela, Khawla A. Al-Matar, Noura K. Al-Saqer, Atta Rahman, Linah Saraireh, Mustafa Youldash and Gomathi Krishnasamy
Sustainability 2023, 15(14), 11138; https://doi.org/10.3390/su151411138 - 17 Jul 2023
Cited by 10 | Viewed by 3914
Abstract
Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone [...] Read more.
Effective waste management and recycling are essential for sustainable development and environmental conservation. It is a global issue around the globe and emerging in Saudi Arabia. The traditional approach to waste sorting relies on manual labor, which is both time-consuming, inefficient, and prone to errors. Nonetheless, the rapid advancement of computer vision techniques has paved the way for automating garbage classification, resulting in enhanced efficiency, feasibility, and management. In this regard, in this study, a comprehensive investigation of garbage classification using a state-of-the-art computer vision algorithm, such as Convolutional Neural Network (CNN), as well as pre-trained models such as DenseNet169, MobileNetV2, and ResNet50V2 has been presented. As an outcome of the study, the CNN model achieved an accuracy of 88.52%, while the pre-trained models DenseNet169, MobileNetV2, and ResNet50V2, achieved 94.40%, 97.60%, and 98.95% accuracies, respectively. That is considerable in contrast to the state-of-the-art studies in the literature. The proposed study is a potential contribution to automating garbage classification and to facilitating an effective waste management system as well as to a more sustainable and greener future. Consequently, it may alleviate the burden on manual labor, reduce human error, and encourage more effective recycling practices, ultimately promoting a greener and more sustainable future. Full article
(This article belongs to the Special Issue Organic Waste Treatment, Recycling, and Reuse)
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15 pages, 2672 KiB  
Article
Potential Hormetic Effects of Cimetidine on Aerobic Composting of Human Feces from Rural China
by Xiaowei Li, Xuan Wang, Xusheng Pan, Ping Zhu, Qianzhi Zhang, Xiang Huang, Xiuquan Deng, Zhipu Wang, Yao Ding, Ximing Liu and John L. Zhou
Sustainability 2022, 14(21), 14454; https://doi.org/10.3390/su142114454 - 03 Nov 2022
Cited by 1 | Viewed by 1285
Abstract
Aerobic composting is widely used worldwide as a natural process for handling human waste. Such waste often contains pharmaceutical residues from human consumption, yet their impact on composting has not been studied. The aim of this study is to investigate the impact of [...] Read more.
Aerobic composting is widely used worldwide as a natural process for handling human waste. Such waste often contains pharmaceutical residues from human consumption, yet their impact on composting has not been studied. The aim of this study is to investigate the impact of the antihistamine cimetidine (10 mg/kg, 100 mg/kg) on the aerobic composting of human feces. The key results show that 10 mg/kg of cimetidine accelerates temperature increase and moisture removal of the composting substrate. The organic matter in all the groups gradually decreased, and the pH values increased first and then declined with the composting time, with no significant differences between the groups. The NH4+-N concentrations and NH3 emission reached the maximum at 1.5 days and then declined rapidly, while the NO2−-N concentrations increased and then decreased, and the NO3−-N contents tended to increase all the time during the composting. The 100 mg/kg cimetidine caused a higher maximal NH4+-N concentration of compost, and a lower maximal NH3 emission at 1.5 days, while 10 mg/kg cimetidine led to more NO2−-N and NO3−-N contents. In addition, 10 mg/kg cimetidine enhanced the aromatization and humification of dissolved organic matter and promoted the degradation of aliphatic substances. Furthermore, 100 mg/kg cimetidine generated a larger influence on the microorganisms than 10 mg/kg cimetidine, especially for the microorganisms related to nitrogen transformation. The findings imply that cimetidine has a dose-dependent impact on the decomposition of organic matter and the conversion of nitrogen in human feces during composting. It deserves further investigation of the possible hormesis effect. Full article
(This article belongs to the Special Issue Organic Waste Treatment, Recycling, and Reuse)
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23 pages, 1400 KiB  
Article
An Artificial Neural Network for Simulation of an Upflow Anaerobic Filter Wastewater Treatment Process
by Mark McCormick
Sustainability 2022, 14(13), 7959; https://doi.org/10.3390/su14137959 - 29 Jun 2022
Cited by 6 | Viewed by 1272
Abstract
The purpose of this work was to develop a problem-solving approach and a simulation tool that is useful for the specification of wastewater treatment process equipment design parameters. The proposition of using an artificial neural network (ANN) numerical model for supervised learning of [...] Read more.
The purpose of this work was to develop a problem-solving approach and a simulation tool that is useful for the specification of wastewater treatment process equipment design parameters. The proposition of using an artificial neural network (ANN) numerical model for supervised learning of a dataset and then for process simulation on a new dataset was investigated. The effectiveness of the approach was assessed by evaluating the capacity of the model to distinguish differences in the equipment design parameters. To demonstrate the approach, a mock dataset was derived from experimentally acquired data and physical effects reported in the literature. The mock dataset comprised the influent flow rate, the bed packing material dimension, the type of packing material and the packed bed height-to-diameter ratio as predictors of the calorific value reduction. The multilayer perceptron (MLP) ANN was compared to a polynomial model. The validation test results show that the MLP model has four hidden layers, each having 256 units (nodes), accurately predicts calorific value reduction. When the model was fed previously unseen test data, the root-mean-square error (RMSE) of the predicted responses was 0.101 and the coefficient of determination (R2) was 0.66. The results of simulation of all 125 possible combinations of the 3 mechanical parameters and identical influent wastewater flow profiles were ranked according to total calorific value reduction. A t-test of the difference between the mean calorific value reduction of the two highest ranked experiments showed that the means are significantly different (p-value = 0.011). Thus, the model has the capacity to distinguish differences in the equipment design parameters. Consequently, the values of the three mechanical feature parameters from the highest ranked simulated experiment are recommended for use in the design of the industrial scale upflow anaerobic filter (UAF) for wastewater treatment. Full article
(This article belongs to the Special Issue Organic Waste Treatment, Recycling, and Reuse)
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