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Sustainability
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Bioprocess Advances for Renewable Energy and Environmental Sustainability
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Bioprocess Advances for Renewable Energy and Environmental Sustainability

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (15 August 2022) | Viewed by 11127

Special Issue Editors

Dr. Sanjeev Kumar Prajapati

E-Mail Website
Guest Editor
Department of Hydro and Renewable Energy, Roorkee, India
Interests: environmental bioprocess; biofuel; wastewater treatment; algal technology; bioenergy; technoeconomic assessment
Dr. Halis Simsek

E-Mail Website
Guest Editor
Department of Agricultural & Biological Engineering, Purdue University, 225 South University Street, West Lafayette, IN 47907-2093, USA
Interests: biological engineering; environmental and natural resources engineering

Special Issue Information

Dear Colleagues,

In recent decades, bioengineering and bioprocess applications for energy and environment sustainability have attracted attention at a global scale. Some of the major applications of bioprocess are toward the integration of environmental management practices (such as wastewater treatment and solid waste management) with possible resource and energy recovery. The present Special Issue entitled “Bioprocess Advances for Renewable Energy and Environmental Sustainability” is therefore conceptualized to compile recent technological developments in the area of environmental bioprocess and bioenergy.  The scope of the present Special Issue shall cover the following topics (but not limited to):

  • Integrated bioprocesses for waste management, recycling, and reuse;
  • Use of bioinspired materials (such as biogenic NPs) for wastewater treatment and energy generation;
  • Recent research interventions on biofuel, bioenergy, and environmental sustainability;
  • Nanotechnology applications for bioenergy;
  • Microbial fuel cells for suitable energy;
  • Nature based technologies (such as constructed wetlands, hydroponics, etc.) for wastewater treatment and energy recovery;
  • Use of computational tools (TEA, LCA etc.) on bioprocesses for the environment and energy sustainability.

Dr. Sanjeev Kumar Prajapati
Dr. Halis Simsek
Guest Editors

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

  • bioprocess
  • bioenergy
  • biofuel
  • resource recovery
  • solid waste
  • wastewater
  • microbial process
  • renewable energy
  • biomass
  • circular bioeconomy
  • life cycle assessment

Published Papers (3 papers)

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Research

11 pages, 2927 KiB  
Article
Catalytic Performance of Cow-Dung Sludge in Water Treatment Mitigation and Conversion of Ammonia Nitrogen into Nitrate
by Lokesh Kumar, Jaigopal Sharma and Raminder Kaur
Sustainability 2022, 14(4), 2183; https://doi.org/10.3390/su14042183 - 14 Feb 2022
Cited by 2 | Viewed by 4029
Abstract
The present research study was performed to find a solution for the mitigation of ammonia nitrogen in municipally treated sewage effluent (MTSE) using two natural bio-resources. One was composted cow-dung sludge that had catalytic enzymes and nitrifying bacterial mass, and the second was [...] Read more.
The present research study was performed to find a solution for the mitigation of ammonia nitrogen in municipally treated sewage effluent (MTSE) using two natural bio-resources. One was composted cow-dung sludge that had catalytic enzymes and nitrifying bacterial mass, and the second was Yucca extract (a desert plant). MTSE samples put in one-liter beakers/jars having initial ammonia nitrogen content of 34.78 mg/L, when treated with 0.0 g/L (control Sample), 1 g/L, 5 g/L (cowdung) and 10 mg/L, 50 mg/L (Yucca extract), respectively, reported depletion of ammonia nitrogen to about 0.00 mg/L ammonia (NH3) as N. It (NH3) transformed to 17.8, 0.18, 0.09, 18.65, and 18.85 mg/L nitrite asN. Ammonia converted to 21.8, 110.1, 133.5, 20.5, 20.8 mg/L nitrate asNO3, respectively. After eight days of treatment, the jar test apparatus reported the results at 35 rounds per minute (RPM) and a temperature of 32 °C. It was found that digested cowdung acted catalytically in eliminating the ammonia nitrogen by converting it to nitrate in a short period of nearly eight days, leading to almost 100% ammonia conversion. Full article
(This article belongs to the Special Issue Bioprocess Advances for Renewable Energy and Environmental Sustainability)
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13 pages, 2370 KiB  
Article
The Economic and Environmental Impact of Greenhouse Heating Pipe Insulation
by Erdem Küçüktopcu, Bilal Cemek and Halis Simsek
Sustainability 2022, 14(1), 549; https://doi.org/10.3390/su14010549 - 5 Jan 2022
Cited by 1 | Viewed by 2404
Abstract
This study aimed to determine the effect of optimum pipe insulation thickness on energy savings and air pollution under greenhouse conditions. In this regard, an optimization model based on a Life Cycle Cost (LCC) analysis was carried out using the P1–P2 method. Three [...] Read more.
This study aimed to determine the effect of optimum pipe insulation thickness on energy savings and air pollution under greenhouse conditions. In this regard, an optimization model based on a Life Cycle Cost (LCC) analysis was carried out using the P1–P2 method. Three fuel types, coal, natural gas, and fuel oil, were tested with nominal pipe sizes ranging from 25 to 65 mm, and hot water was used in the system. Our findings showed that the highest insulation thickness (0.807 m), the greatest energy savings ($62.351/m), and the lowest payback period (0.502 years) were achieved with a 65 mm pipe size for fuel oil. Overall, the insulation minimizes heat loss through the heating pipelines, resulting in economic and environmental benefits. Fuel oil was determined as the best option for savings in this study. Hence, for fuel oil utilization, the emissions of CO2 varied from 2.762 to 3.798 kg/m and SO2 from 0.014 to 0.020 kg/m for pipe thicknesses ranging from 25 and 65 mm, respectively. Full article
(This article belongs to the Special Issue Bioprocess Advances for Renewable Energy and Environmental Sustainability)
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16 pages, 7695 KiB  
Article
Deep Learning Models to Determine Nutrient Concentration in Hydroponically Grown Lettuce Cultivars (Lactuca sativa L.)
by Mostofa Ahsan, Sulaymon Eshkabilov, Bilal Cemek, Erdem Küçüktopcu, Chiwon W. Lee and Halis Simsek
Sustainability 2022, 14(1), 416; https://doi.org/10.3390/su14010416 - 31 Dec 2021
Cited by 11 | Viewed by 3059
Abstract
Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce [...] Read more.
Deep learning (DL) and computer vision applications in precision agriculture have great potential to identify and classify plant and vegetation species. This study presents the applicability of DL modeling with computer vision techniques to analyze the nutrient levels of hydroponically grown four lettuce cultivars (Lactuca sativa L.), namely Black Seed, Flandria, Rex, and Tacitus. Four different nutrient concentrations (0, 50, 200, 300 ppm nitrogen solutions) were prepared and utilized to grow these lettuce cultivars in the greenhouse. RGB images of lettuce leaves were captured. The results showed that the developed DL’s visual geometry group 16 (VGG16) and VGG19 architectures identified the nutrient levels of lettuces with 87.5 to 100% accuracy for four lettuce cultivars, respectively. Convolution neural network models were also implemented to identify the nutrient levels of the studied lettuces for comparison purposes. The developed modeling techniques can be applied not only to collect real-time nutrient data from other lettuce type cultivars grown in greenhouses but also in fields. Moreover, these modeling approaches can be applied for remote sensing purposes to various lettuce crops. To the best knowledge of the authors, this is a novel study applying the DL technique to determine the nutrient concentrations in lettuce cultivars. Full article
(This article belongs to the Special Issue Bioprocess Advances for Renewable Energy and Environmental Sustainability)
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