Wastewater-Grown Algal Biomass as Carbon-neutral, Renewable, and Low Water Footprint Feedstock for Clean Energy and Bioplastics

Mehmood, Muhammad Aamer; Amin, Mahwish; Haider, Muhammad Nabeel; Malik, Sana; Malik, Hafiza Aroosa; Alam, Md. Asraful; Xu, Jingliang; Alessa, Abdulrahman H.; Khan, Aqib Zafar; Boopathy, Raj

doi:10.1007/s40726-024-00294-x

Cited by 2 publications

(1 citation statement)

References 104 publications

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“…Specialized treatment technologies exist for tannery wastewater. However, they have drawbacks like ineffective metal removal at low concentrations and high chemical consumption. − Biotechnological approaches show promise, utilizing microorganisms for heavy metal remediation. − Microalgae, particularly Spirulina , are gaining attention for their pollutant abatement capabilities. − Spirulina’s integration into cleaner production and sustainability practices has gained traction, with studies highlighting its potential in wastewater treatment, carbon sequestration, and biofuel production. ,− Its rapid growth and high nutrient uptake efficiency make it effective for bioremediation. ,− Spirulina cultivation offers sustainable food and feed production, requiring minimal resources compared with conventional crops. Additionally, Spirulina -derived products contribute to a circular economy, promoting resource efficiency and waste valorization. − The literature reports the kinetic model of Spirulina based on the ability to remove metal ions, nutrients, and specific growth rates. − As per our knowledge, there is limited information on the growth model, which incorporates nutrient adsorption, light absorption, photosynthetic efficiency, respiration, and biosynthesis efficiency during synthetic or real wastewater cultivation.…”

Section: Introductionmentioning

confidence: 99%

Unlocking the Potential: Algal Biomass Cultivation and Growth Kinetics Using Tanning Process Water

Gagrai

2024

ACS Sustainable Resour. Manage.

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A basic kinetic model has been applied to microalgae to predict the growth parameters in Zarrouk Media composition (ZMC) and precipitated chrome tanning effluent (PCTE). The model was fitted with the experimental data of Spirulina cultivation to estimate growth parameters: nutrient adsorption constant (K a) (h–1), nutrient desorption constant (K d) (h–1), rate of respiration (rR c)(h–1), efficiency of bio-synthesis (β) (g g–1), respiration rate (h–1)(rR c), rate of maximum photosynthesis (p max) (h–1), coefficient for light-absorption (α) (m2 g–1), photon efficiency (g.μmol–1 photons–1) (φm), etc. The model suggests a higher nutrient adsorption rate in ZMC (0.75 h–1) as compared to PCTE (1.40 × 10–2 h–1). The rate of respiration of Spirulina decreased due to cultivation in PCTE from 5.36 × 10–3 to 1.91 × 10–3 h–1. The biosynthetic efficiency of Spirulina decreased from 8.72 to 2.002 due to cultivation in PCTE media. The maximum photosynthetic rate h–1 was slightly higher in ZMC as compared to PCTE media. The model parameter values were lower for Spirulina in PCTE than those in ZMC. Spirulina’s cell density was lower in PCTE compared to ZMC, as the doubling time was increased from 9.97 h–1 to 31.47 h–1. Moreover, the optimum pH for growth was also shifted from 9.5 to 10.5. The higher dose of PCTE (Cl– > 2.96 × 103 mg L–1) restricted cell growth. Adding Cr(III) in ZMC has a higher impact on cell growth than Cr(VI). The model parameters with Cr(III) and Cr(VI) also showed a decrease in values except rRc and β have higher values for Cr(VI) as compared to Cr(III) added ZMC due to the non-interaction of Cr(VI) with algae. The model-predicted cell growth rates closely align with experimental results, with deviations within an ±7% margin. The addition of heavy metals to ZMC disrupts nutrient interactions and transport mechanisms during Spirulina cell growth.

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Section: Introductionmentioning

confidence: 99%

Unlocking the Potential: Algal Biomass Cultivation and Growth Kinetics Using Tanning Process Water

Gagrai

2024

ACS Sustainable Resour. Manage.

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Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

Malashin,

Martysyuk,

Tynchenko

et al. 2024

Polymers

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The integration of machine learning (ML) into material manufacturing has driven advancements in optimizing biopolymer production processes. ML techniques, applied across various stages of biopolymer production, enable the analysis of complex data generated throughout production, identifying patterns and insights not easily observed through traditional methods. As sustainable alternatives to petrochemical-based plastics, biopolymers present unique challenges due to their reliance on variable bio-based feedstocks and complex processing conditions. This review systematically summarizes the current applications of ML techniques in biopolymer production, aiming to provide a comprehensive reference for future research while highlighting the potential of ML to enhance efficiency, reduce costs, and improve product quality. This review also shows the role of ML algorithms, including supervised, unsupervised, and deep learning algorithms, in optimizing biopolymer manufacturing processes.

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Wastewater-Grown Algal Biomass as Carbon-neutral, Renewable, and Low Water Footprint Feedstock for Clean Energy and Bioplastics

Cited by 2 publications

References 104 publications

Unlocking the Potential: Algal Biomass Cultivation and Growth Kinetics Using Tanning Process Water

Unlocking the Potential: Algal Biomass Cultivation and Growth Kinetics Using Tanning Process Water

Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

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