Search Results (57)

Industrial wastewater management remains a critical barrier to achieving Sustainable Development Goal 6 (SDG 6) in many developing countries, where regulatory frameworks exist but affordable and scalable treatment solutions are lacking. In Pakistan, the textile sector is a leading polluter, with untreated effluents routinely discharged into rivers and agricultural lands despite stringent National Environmental Quality Standards (NEQS). This study presents a pilot-scale case from Faisalabad’s Khurrianwala industrial zone, where a decentralized, nature-based bioreactor was piloted to bridge the gap between policy and practice. The system integrates four treatment stages—anaerobic digestion (AD), floating treatment wetland (FTW), constructed wetland (CW), and sand filtration (SF)—and was further intensified via nutrient amendment, aeration, and bioaugmentation with three locally isolated bacterial strains (Acinetobacter junii NT-15, Pseudomonas indoloxydans NT-38, and Rhodococcus sp. NT-39). The fully intensified configuration achieved substantial reductions in total dissolved solids (TDS) (46%), total suspended solids (TSS) (51%), chemical oxygen demand (COD) (91%), biochemical oxygen demand (BOD) (94%), nutrients, nitrogen (N), and phosphorus (P) (86%), sulfate (26%), and chloride (41%). It also removed 95% iron (Fe), 87% cadmium (Cd), 57% lead (Pb), and 50% copper (Cu) from the effluent. The bacterial inoculants persist in the system and colonize the plant roots, contributing to stable bioremediation. The treated effluent met the national environmental quality standards (NEQS) discharge limits, confirming the system’s regulatory and ecological viability. This case study demonstrates how nature-based systems, when scientifically intensified, can deliver high-performance wastewater treatment in industrial zones with limited infrastructure—offering a replicable model for sustainable, SDG-aligned pollution control in the Global South. Full article

Microalgae have long been recognized for their potential in biofuel production and wastewater treatment, but their broader capabilities remain underexplored. This opinion paper presents a case for a significant shift in how microalgae are conceptualized from biomass producers to dynamic, multifunctional systems that can serve as environmental nano-factories. It highlights emerging research on the role of microalgae in heavy metal sequestration, the green biosynthesis of metal nanoparticles, and the cascading valorization of residual biomass through environmentally sustainable extraction methods. Together, these applications offer a unified platform for pollution mitigation and the production of valuable materials. The paper also examines recent progress in synthetic biology, bioreactor design, and microbial consortia that could support this transition. At the same time, it acknowledges key challenges, including issues of scalability, regulatory acceptance, and process integration. Strategic recommendations are proposed to advance this field and align it more closely with circular economy models. By reimagining microalgae as living nano-factories, this paper outlines a path forward for developing integrated, sustainable technologies that simultaneously address environmental and industrial challenges. Full article

Uneven distribution and delayed system response of dissolved oxygen (DO) in dual−tank recirculating bioreactor systems pose significant challenges for oxygen supply. To address these issues, a dynamic parameterized predictive control (DPPC) approach is proposed and validated through simulation and bench−scale experiments. This method is underpinned by a mathematical model that integrates mass transfer kinetics and chemical thermodynamic principles, accurately capturing oxygen dissolution and transfer within a recirculating environment. By predicting future DO variations and continuously integrating real−time monitoring data, the controller adjusts oxygen injection parameters in real time, rapidly restoring DO levels to target values while minimizing overshoot and latency introduced by system circulation. Experimental results in dual−tank setups show an RMSE below 0.05 and an R² exceeding 0.99, affirming the model’s predictive accuracy under varying oxygen conditions. Compared with conventional feedback control strategies, the proposed method demonstrates improved stability, faster response, and lower overshoot, achieving a 47.8% reduction in ISE and a 41.4% reduction in IAE, thus highlighting its superior tracking accuracy. These findings suggest the DPPC method holds promise as a control framework for future application in oxygen−sensitive culture systems. Full article

The European Union views biogas production from landfills as a crucial element in achieving decarbonization goals by 2050. Biogas is primarily composed of methane (CH₄) and carbon dioxide (CO₂), produced through the anaerobic digestion of various residual materials. This study aimed to investigate CH₄ and CO₂ concentrations from municipal solid waste in biogas capture wells in a landfill in Romania between 2023 and 2024. A peak in CH₄ concentrations occurred in the fall of 2024 (P4 well), while the highest CO₂ content was recorded in the summer of 2023 (P3 well). The Aermod View software platform (version 11.2.0) was employed to model the dispersion of pollutants in the surrounding air. A worst-case scenario was applied to estimate the highest ground-level pollutant concentrations. The highest recorded CH₄ concentration was 90.1 mg/m³, while CO₂ reached 249 mg/m³ within the landfill. The highest CH₄ concentrations were found in the southern part of the site, less than 1 km from the landfill, while CO₂ was highest in the northern area. In conclusion, municipal solid waste landfills behave like unpredictable bioreactors, and without proper management and oversight, they can pose significant risks. An integrated system that combines prevention, reuse, and correct disposal is critical to minimizing these negative effects. Full article

This study investigated the potential of Yarrowia lipolytica, an oleaginous yeast, for producing lipid-rich biomass and its application in food technology. According to EFSA guidelines, lipid-rich biomass is recognized as a novel food with potential nutritional and technological value. However, cost-effective and scalable production of such biomass remains a challenge. The yeast was cultured in a nitrogen-limited medium using a cost-containment strategy based on the use of waste carbon sources, such as post-frying oil and untreated tap water. The composed batch culture approach studied in the experiments presented an example that reduces the cost of yeast biomass biosynthesis. This research aimed to characterize the biomass to assess its nutritional quality and suitability for food applications. Cultures were conducted in a laboratory bioreactor with a working volume of 4 litres. Key kinetic parameters were determined, including biomass yield (X), maximum lipid concentration (Lmax), lipid yield, protein yield relative to substrate and the specific rate of lipid synthesis or protein content and other cellular components. The biomass of Y. lipolytica demonstrated a high lipid content (39.43–50.53%), with significant levels of protein (24.16–27.03%) and unsaturated fatty acids, including oleic acid (62.73–66.44%) and linoleic acid (19.40–21.40%). Lipid-rich biomass produced in cultures with shorter times (20 h), which ended in the logarithmic growth phase, exhibited lower oxidative stability than longer cultures (65 h), which ended in the stationary growth phase. The results of this study highlighted that waste carbon sources and untreated tap water did not significantly impact the biomass yield or the nutritional profile, but did affect the stability of the produced oil. The biomass of Y. lipolytica, containing over 20% lipids, could serve as a promising raw material for food technology, providing a sustainable alternative to traditional vegetable oils. This work makes an important contribution to the development of alternative lipid sources by integrating waste processing in bioreactor-scale culture and kinetic modelling. Full article

This study presents the long-term performance evaluation of a full-scale hybrid membrane bioreactor (MBR)–nanofiltration (NF) system for the treatment of high-strength municipal landfill leachate from the Istanbul–Şile Kömürcüoda facility. Over a 16-month operational period, influent and effluent samples were analyzed for key parameters, including chemical oxygen demand (COD), ammonium nitrogen (NH₄⁺-N), total phosphorus (TP), suspended solids (SS), and temperature. The MBR unit consistently achieved high removal efficiencies for COD and NH₄⁺-N (93.5% and 98.6%, respectively), while the NF stage provided effective polishing, particularly for phosphorus, maintaining a TP removal above 95%. Seasonal analysis revealed that the biological performance peaked during spring, likely due to optimal microbial conditions. To support intelligent control strategies, artificial neural network (ANN) models were developed to predict effluent COD and NH₄⁺-N concentrations using influent and operational parameters. The best-performing ANN models achieved R² values of 0.861 and 0.796, respectively. The model’s robustness was validated through RMSE, MAE, and 95% confidence intervals. Additionally, Principal Component Analysis (PCA) and Random Forest algorithms were employed to determine the parameter importance and nonlinear interactions. The findings demonstrate that the integration of hybrid membrane systems with AI-based modeling can enhance treatment efficiency and forecasting capabilities for landfill leachate management, offering a resilient and data-driven approach to sustainable operation. Full article

Microalgae are capable of synthesizing a diverse range of biologically active compounds, including omega-3 fatty acids, carotenoids, proteins, and polysaccharides, which demonstrate significant value in the fields of functional foods, innovative pharmaceuticals and high-value cosmetics. With advancements in biotechnology and the increasing demand for natural products, studies on the functional components of algae have made significant strides. However, the commercial utilization of algal bioactives still faces challenges, such as low cultivation efficiency, limited component identification, and insufficient health evaluation. Artificial intelligence (AI) has recently emerged as a transformative tool to overcome these technological barriers in the production, characterization, and application of algal bioactive ingredients. This review examines the multidimensional mechanisms by which AI enables and optimizes these processes: (1) AI-powered predictive models, integrated with machine learning algorithms (MLAs), Industry 4.0, and other advanced digital systems, support real-time monitoring and control of intelligent bioreactors, allowing for accurate forecasting of cultivation yields and market demand. (2) AI facilitates in-depth analysis of gene regulatory networks and key metabolic pathways, enabling precise control over the biosynthesis of targeted compounds. (3) AI-based spectral imaging and image recognition techniques enable rapid and reliable identification, classification, and quality assessment of active components. (4) AI accelerates the transition from mass production to the development of personalized medical and functional nutritional products. Collectively, AI demonstrates immense potential in enhancing the yield, refining the characterization, and expanding the application scope of algal bioactives, unlocking new opportunities across multiple high-value industries. Full article

Background/Objectives: Mandibular reconstruction following trauma or oncologic resection is crucial for restoring function and aesthetics. While autologous bone grafting remains the gold standard, it presents challenges such as donor site morbidity and graft availability. Bone tissue engineering (BTE) offers an innovative alternative, integrating scaffolds, osteogenic cells, and bioactive factors to regenerate functional bone. This systematic review evaluates BTE strategies for mandibular reconstruction, focusing on critical-sized defects in large animal models and their translational potential for clinical applications. Methods: A systematic review was performed following PRISMA guidelines. Eligible studies involved large animal models and critical-sized mandibular defects treated with at least two BTE components (scaffold, osteogenic cells, or growth factors). Quality and bias assessments were conducted using ARRIVE guidelines and SYRCLE tools. Results: Of the 6088 studies screened, 27 met the inclusion criteria, focusing on critical-sized mandibular defects in large animal models such as pigs, sheep, and dogs. Common scaffolds included β-tricalcium phosphate (β-TCP), poly-lactic-co-glycolic acid (PLGA), and polycaprolactone (PCL), frequently combined with bone marrow-derived mesenchymal stem cells (BMSCs) and growth factors like recombinant human bone morphogenetic protein-2 (rhBMP-2). Preclinical outcomes demonstrated effective bone regeneration, vascularization, and biomechanical restoration. Advanced strategies, including in vivo bioreactors and 3D-printed scaffolds, further enhanced regeneration. However, challenges such as incomplete scaffold degradation, hypoxic conditions within constructs, and variability in growth factor efficacy and dose optimization were observed, emphasizing the need for further refinement to ensure consistent outcomes. Conclusions: BTE shows promise in mandibular reconstruction, achieving bone regeneration and functional restoration in preclinical models of critical-sized defects. However, challenges such as scaffold optimization, vascularization enhancement, and protocol standardization require further investigation to facilitate clinical translation. These findings emphasize the need for refinement to achieve consistent, scalable outcomes for clinical use. Full article

This work proposes a nonlinear control strategy, an output feedback control based on a sinusoidal control approach for output regulation purposes with application to semi-continuous (fed-batch) bio/chemical processes. A key feature of the proposed control scheme is its auto-stop property, which ensures that the required set points are reached while automatically ceasing control input. This is particularly advantageous in fed-batch reactors, where exceeding the maximum operative volume is a common concern; additionally, the proposed controller can be bounded by only selecting the amplitude of the sine function. The closed-loop stability of the designed auto-stop control law is analyzed via the Lyapunov–Krazovzkii framework, which allows us to claim that the closed-loop dynamic operation of the corresponding processes is stable. The proposed controller is applied to two typical examples of semi-continuous bio/chemical reactors for regulation purposes, which aim to increase the reactors’ productivity. In addition, a comparison with a well-tuned internal model control proportional–integral (IMC PI) controller is performed. To show the performance of the control schemes, numerical experiments were carried out to show the controllers’ performance under different and realistic operation conditions. Here, for the bioreactor example, the performance index does not reach a steady state, but the gap between the IMC PI controller and the proposed one is around 100, 200, and 250 units for the different set points, which is in favor of the proposed controller. Regarding the chemical reactor, the performance index of the corresponding gap between the steady-state values of the performance index is also in favor of the proposed control law. Full article

Fermentation represents a pivotal bioconversion process that enhances foodstuffs’ nutritional and sensory attributes while playing a crucial role in global food systems. Nevertheless, concerns about safety issues associated with microbial contamination and the production of biogenic amines are often understated. This review appraised recent advancements in fermentation technology, emphasising their association with the health and safety of fermented foods. Key advances include predictive microbiology models, in some cases achieving up to 95% accuracy in predicting microbial behaviour, and high-throughput sequencing (HTS) for microbial enrichment. In addition, advanced detection methods such as biosensors and PCR-based assays enable the rapid identification of contaminants, improving manufacturing processes and preserving product integrity. Advanced bioreactor technologies equipped with real-time monitoring systems have been shown to increase fermentation efficiency. Moreover, innovative packaging, artificial intelligence, machine learning models, and sensor technologies have optimised fermentation processes and contributed to tracking quality and safety in the blockchain technology supply chain, potentially reducing spoilage rates and showing a decrease in production times. This study also addresses regulatory frameworks essential for establishing robust safety protocols. Integrating advanced fermentation technologies is imperative to meet the growing global demand for safe fermented foods. Continuous research and innovation are needed to address safety challenges and promote industry practices prioritising health and quality, ensuring public safety and building consumer confidence in fermented products. Full article

We investigated the design and characterization of a Lab-On-a-Chip (LoC) cell detection system primarily designed to support immunotherapy in cancer treatment. Immunotherapy uses Chimeric Antigen Receptors (CARs) and T Cell Receptors (TCRs) to fight cancer, engineering the response of the immune system. In recent years, it has emerged as a promising strategy for personalized cancer treatment. However, it requires bioreactor-based cell culture expansion and manual quality control (QC) of the modified cells, which is time-consuming, labour-intensive, and prone to errors. The miniaturized LoC device for automated QC demonstrated here is simple, has a low cost, and is reliable. Its final target is to become one of the building blocks of an LoC for immunotherapy, which would take the place of present labs and manual procedures to the benefit of throughput and affordability. The core of the system is a commercial, on-chip-integrated capacitive sensor managed by a microcontroller capable of sensing cells as accurately measured charge variations. The hardware is based on standardized components, which makes it suitable for mass manufacturing. Moreover, unlike in other cell detection solutions, no external AC source is required. The device has been characterized with a cell line model selectively labelled with gold nanoparticles to simulate its future use in bioreactors in which labelling can apply to successfully engineered CAR-T-cells. Experiments were run both in the air—free drop with no microfluidics—and in the channel, where the fluid volume was considerably lower than in the drop. The device showed good sensitivity even with a low number of cells—around 120, compared with the 10⁷ to 10⁸ needed per kilogram of body weight—which is desirable for a good outcome of the expansion process. Since cell detection is needed in several contexts other than immunotherapy, the usefulness of this LoC goes potentially beyond the scope considered here. Full article

New Zealand’s agricultural sector faces the challenge of maintaining productivity while minimizing impacts on freshwaters. This study evaluates the cost-effectiveness of various green infrastructure systems designed to reduce diffuse agricultural sediment and nutrient loads. Utilizing a quantitative economic and contaminant reduction modeling approach, we analyze the impacts of five interceptive mitigation systems: riparian grass filter strips, constructed wetlands, woodchip bioreactors, filamentous algal nutrient scrubbers, and detainment bunds. Our approach incorporates Monte Carlo simulations to address uncertainties in costs and performance, integrating hydrological flow paths and contaminant transport dynamics. Mitigation systems are assessed individually and in combination, using a greedy cyclical coordinate descent algorithm to find the optimal combination and scale of a system for a particular landscape. Applying the model to a typical flat pastoral dairy farming landscape, no single system can effectively address all contaminants. However, strategic combinations can align with specific freshwater management goals. In our illustrative catchment, the mean cost to remove the full anthropogenic load is NZD 1195/ha for total nitrogen, NZD 168 for total phosphorus, and NZD 134 for suspended solids, but results will vary considerably for other landscapes. This study underscores the importance of tailored deployment of green infrastructure to enhance water quality and support sustainable agricultural practices. Full article

Large-scale bioprocesses are increasing globally to cater to the larger market demands for biological products. As fermenter volumes increase, the efficiency of mixing decreases, and environmental gradients become more pronounced compared to smaller scales. Consequently, the cells experience gradients in process parameters, which in turn affects the efficiency and profitability of the process. Computational fluid dynamics (CFD) simulations are being widely embraced for their ability to simulate bioprocess performance, facilitate bioprocess upscaling, downsizing, and process optimisation. Recently, CFD approaches have been integrated with dynamic Cell reaction kinetic (CRK) modelling to generate valuable information about the cellular response to fluctuating hydrodynamic parameters inside large production processes. Such coupled approaches have the potential to facilitate informed decision-making in intelligent biomanufacturing, aligning with the principles of “Industry 4.0” concerning digitalisation and automation. In this review, we discuss the benefits of utilising integrated CFD-CRK models and the different approaches to integrating CFD-based bioreactor hydrodynamic models with cellular kinetic models. We also highlight the suitability of different coupling approaches for bioprocess modelling in the purview of associated computational loads. Full article

Acute and chronic liver failure are clinically significant conditions, and the artificial liver support system (ALSS) is emerging as a novel and effective approach for the clinical management of liver failure. Within this framework, scaffold materials occupy a pivotal position as integral components of the bioreactor. Elevating the performance capabilities of these scaffolds not only augments the therapeutic efficacy of the artificial liver but also lays the groundwork for refining and selecting large-scale hepatocyte culture models. In this study, we introduced a novel hepatocyte scaffold material designated as PET-COL, crafted by coating polyethylene terephthalate (PET) with collagen. This involved a sequence of modifications, including alkaline hydrolysis, EDC/NHS activation and crosslinking, as well as collagen conjugation. The physicochemical attributes of the scaffold were thoroughly characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), second harmonic generation (SHG), water contact angle measurements, and high-performance liquid chromatography–mass spectrometry (HPLC-MS). Furthermore, an investigation into the material’s biological properties was conducted that encompassed SEM (HepaRG growth), fluorescence staining (assessment of cell viability), staining by trypan blue (HepaRG counting), CCK-8 (proliferation of cells), biochemical testing, and immunosorbent assay. Our findings revealed that collagen was covalently bonded to the PET surface, leading to a substantial enhancement in the material’s hydrophilicity (p < 0.001). The quantity of collagen coating was determined to be precisely 33.30 μg per scaffold. Human liver progenitor HepaRG cells thrived on the PET-COL material. Compared with the untreated group, cell viability, albumin secretion, urea synthesis, and the expression levels of CYP3A4 and CPS1 increased significantly (p < 0.001), demonstrating remarkable biological vitality. The PET-COL scaffold, as developed in this study, holds immense potential for application in bioartificial livers. Full article

The propagation of Crocus sativus L. relies exclusively on corm multiplication. As underground storage organs, corms are susceptible to a wide range of pathogens, environmental stresses, and diseases, making traditional propagation methods often ineffective with the loss of valuable material. In vitro propagation offers an alternative for the saffron culture under controlled conditions. In particular, the innovative application of the Temporary Immersion System (TIS) represents a technological advancement for enhancing biomass production with a reduction in operational costs. The current study utilized the Plantform™ bioreactor to propagate in vitro saffron corms from the ‘Abruzzo’ region (Italy), integrating machine learning models to assess its performance. The evaluation of saffron explants after 30, 60, and 90 days of culture showed a marked improvement in growth and microcorm production compared to conventional in vitro culture on semisolid medium, supported by the machine learning analysis. Indeed, the Random Forest algorithm revealed a predictive accuracy with an R² value of 0.81 for microcorm number, showcasing the capability of machine learning models to forecast propagation outcomes effectively. These results confirm that applying TIS in saffron culture could lead to economically viable, large biomass production within a controlled environment, irrespective of seasonality. This study represents the first endeavor to use TIS technology to enhance the in vitro propagation of saffron in conjunction with machine learning, suggesting an innovative approach for cultivating high-value crops like saffron. Full article