Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

Investigation of self-regulation mechanisms of extracellular organic matters in reused medium on Spirulina platensis

Recirculating the cultivation medium of Spirulina platensis (S. platensis) enables efficient water and nutrient recycling, thereby reducing production costs. To figure out the inhibition components of the reused medium and cell oxidate response, this study delves into the metabolic regulation of the reused medium and its extracted organic matters (OMs) and extracellular polysaccharides (EPS) on S. platensis. The reused medium and the medium containing dissolved OMs and EPS significantly increased oxidative stress in S. platensis, reducing biomass production with inhibition rates ranging from 18.08 % to 26.59 %. Nevertheless, the incorporation of EPS from OMs augmented the synthesis of proteins, polyphenols, and chlorophyll in S. platensis, sustaining photosynthetic activity and a higher proportion of live cells. Future research should prioritize the characterization of OMs and EPS, mitigate the inhibitory effects of OMs extracted residue (molecular weight < 1000 Da), further optimize the recyclability of the reused medium, and enhance S. platensis's functional composition.

Advances in innovative extraction techniques for polysaccharides, peptides, and polyphenols from distillery by-products: Common extraction techniques, emerging technologies, and AI-driven optimization

Distillery by-products, such as distillers' grains, stillage, and vinasse, are rich in organic compounds and offer immense potential for the recovery of bioactive substances, including polysaccharides, peptides, and polyphenols. The effective utilization of these by-products is critical for achieving long-term sustainability in the distillery sector. This review highlights advancements in extraction techniques, focusing on enzymatic, ultrasound-assisted, and microwave-assisted methods while also exploring emerging approaches such as supercritical fluid extraction, pressurized liquid extraction, pulse electric field, and synthetic biology. These innovative techniques address the limitations of traditional methods by improving extraction yields, reducing processing times, and enhancing sustainability. Additionally, the integration of machine learning and artificial intelligence is discussed as a promising avenue for optimizing extraction parameters and scaling up processes. By evaluating recent achievements and identifying new opportunities, this study aims to promote sustainable practices in the distillery industry, emphasizing economic feasibility, environmental impacts, and resource optimization for value-added product development.

The analysis of optimization in music aesthetic education under artificial intelligence

In the artificial intelligence (AI) domain, effectively integrating deep learning (DL) technology with the content, teaching methodologies, and learning processes of music aesthetic education remains a subject worthy of in-depth exploration and discussion. The aim is to meet to the music aesthetic needs of students across different age groups and levels of musical literacy. In this paper, the concepts of AI and DL algorithm are first introduced, and their algorithm principles and application status are understood. Then, they are integrated into the application of music aesthetic education, and the algorithm principles and running codes are designed. Finally, experiments are carried out to verify the accuracy of music emotion recognition based on DL algorithm in AI environment to verify the effectiveness of music aesthetic education method based on DL. The results show that the algorithm proposed in this paper has higher accuracy, which combines the advantages of AI and DL algorithm, and obtains higher recognition accuracy. It provides more possibilities for future music aesthetic teaching activities. This paper is dedicated to investigating the feasibility and approach to optimizing the method of music aesthetic education through DL. Its objective is to chart a new developmental direction and practical pathway for music aesthetic education in the era of AI.

Advancements and hurdles in symbiotic microalgal co-cultivation strategies for wastewater treatment

Microalgae offer significant potential in various industrial applications, such as biofuel production and wastewater treatment, but the economic barriers to their cultivation and harvesting have been a major obstacle. However, a promising strategy involving co-cultivating microalgae in wastewater treatment could overcome the limitations of monocultivation and open the possibility for increased integration of microalgae into various industrial processes. This symbiotic relationship between microalgae and other microbes can enhance nutrient removal efficiency, increase value-added bioproduct production, promote carbon capture, and decrease energy consumption. However, unresolved challenges, such as the competition between microalgae and other microbes within the wastewater treatment system, may result in imbalances and reduced efficiency. The complexity of managing multiple microbes in a co-cultivation system poses difficulties in achieving stability and consistency in bioproduct production. In response to these challenges, strategies such as optimizing nutrient ratios, manipulating environmental conditions, understanding the dynamics of microbial relationships, and employing genetic modification to enhance the metabolic capabilities of microalgae and improve their competitiveness are critical in transitioning to a more sustainable path. Hence, this review will provide an in-depth analysis of recent advancements in symbiotic microalgal co-cultivation for applications in wastewater treatment and CO2 utilization, as well as discuss approaches for improving microalgal strains through genetic modification. Furthermore, the review will explore the use of efficient bioreactors, advanced control systems, and advancements in biorefinery processes.

Advancements of astaxanthin production in Haematococcus pluvialis: Update insight and way forward

The global market demand for natural astaxanthin (AXT) is growing rapidly owing to its potential human health benefits and diverse industry applications, driven by its safety, unique structure, and special function. Currently, the alga Haematococcus pluvialis (alternative name H. lacustris) has been considered as one of the best large-scale producers of natural AXT. However, the industry's further development faces two main challenges: the limited cultivation areas due to light-dependent AXT accumulation and the low AXT yield coupled with high production costs resulting from complex, time-consuming upstream biomass culture and downstream AXT extraction processes. Therefore, it is urgently to develop novel strategies to improve the AXT production in H. pluvialis to meet industrial demands, which makes its commercialization cost-effective. Although several strategies related to screening excellent target strains, optimizing culture condition for high biomass yield, elucidating the AXT biosynthetic pathway, and exploiting effective inducers for high AXT content have been applied to enhance the AXT production in H. pluvialis, there are still some unsolved and easily ignored perspectives. In this review, firstly, we summarize the structure and function of natural AXT focus on those from the algal H. pluvialis. Secondly, the latest findings regarding the AXT biosynthetic pathway including spatiotemporal specificity, transport, esterification, and storage are updated. Thirdly, we systematically assess enhancement strategies on AXT yield. Fourthly, the regulation mechanisms of AXT accumulation under various stresses are discussed. Finally, the integrated and systematic solutions for improving AXT production are proposed. This review not only fills the existing gap about the AXT accumulation, but also points the way forward for AXT production in H. pluvialis.

Microalgae-based bioremediation of refractory pollutants: an approach towards environmental sustainability

Extensive anthropogenic activity has led to the accumulation of organic and inorganic contaminants in diverse ecosystems, which presents significant challenges for the environment and its inhabitants. Utilizing microalgae as a bioremediation tool can present a potential solution to these challenges. Microalgae have gained significant attention as a promising biotechnological solution for detoxifying environmental pollutants. This is due to their advantages, such as rapid growth rate, cost-effectiveness, high oil-rich biomass production, and ease of implementation. Moreover, microalgae-based remediation is more environmentally sustainable for not generating additional waste sludge, capturing atmospheric CO2, and being efficient for nutrient recycling and sustainable algal biomass production for biofuels and high-value-added products generation. Hence, microalgae can achieve sustainability's three main pillars (environmental, economic, and social). Microalgal biomass can mediate contaminated wastewater effectively through accumulation, adsorption, and metabolism. These mechanisms enable the microalgae to reduce the concentration of heavy metals and organic contaminants to levels that are considered non-toxic. However, several factors, such as microalgal strain, cultivation technique, and the type of pollutants, limit the understanding of the microalgal removal mechanism and efficiency. Furthermore, adopting novel technological advancements (e.g., nanotechnology) may serve as a viable approach to address the challenge of refractory pollutants and bioremediation process sustainability. Therefore, this review discusses the mechanism and the ability of different microalgal species to mitigate persistent refractory pollutants, such as industrial effluents, dyes, pesticides, and pharmaceuticals. Also, this review paper provided insight into the production of nanomaterials, nanoparticles, and nanoparticle-based biosensors from microalgae and the immobilization of microalgae on nanomaterials to enhance bioremediation process efficiency. This review may open a new avenue for future advancing research regarding a sustainable biodegradation process of refractory pollutants.

Integrating material flow analysis into hydrological model for water environment management of large-scale urban-rural mixed catchment

Simultaneous simulation of urban and rural hydrological processes is important for water environment management of mixed land-uses catchments. However, the discharge paths of pollution in the urban drainage system are not described in traditional catchment hydrological models. In this study, an urban-rural water environment (URWE) model is developed through incorporating the material flow analysis (MFA) and the soil and water assessment tool (SWAT) into a general framework. The URWE model is an advancement with respect to traditional hydrological models in terms of simultaneously simulating the urban organized and rural decentralized discharges of pollution. Due to the low data requirement and high computational efficiency of MFA, URWE model is applicable to large-scale catchment with wide urban area. The URWE model is applied to a typical urban-rural mixed catchment, the Dianchi Catchment (China), where the pollution characteristics are analyzed and the pollution control measures are investigated. Results indicate that the URWE model outperforms the conventional SWAT model for both water quantity and quality simulations, with an 8.5 % improvement in average coefficient of determination (R2) and a 67.4 % improvement in average Nash coefficient (NSE). Rural best management practice, rainwater-sewage separation, and storage capacity expansion are identified as the most cost-effective measures for COD, TN, and TP reduction, respectively. Contributions of this study are to improve the accuracy of water environment simulation in urban-rural mixed catchment, as well as to help decision-makers develop synergistic urban-rural water environment management measures.

Enhancing the content of phycoerythrin through the application of microplastics from Porphyridium cruentum produced in wastewater using machine learning methods

Microalgae can produce secondary metabolites like phycoerythrin (Phy). The effects of some microplastics (MPs), wastewater (WW), and light intensity (LI) parameters, including complex data sets, on Phy concentration from Porphyridium cruentum were investigated using machine learning methods in this study. Also, the deep learning (DL) model was developed to get the maximum phy concentration from the dataset. The dataset (232 data groups), including a feature set, polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), WW, LI, and an output variable, Phy, were randomly divided into training and test sets to create and evaluate the models. The highest experimental and predicted Phy concentrations were 52.3 mg/g and 58.32 mg/g in a scenario with 15% WW, 80 mg/L PE, PP, PS, and PVC, and a LI of 175 μmolm-2 s-1, respectively. The Pearson correlation coefficient (r) indicates a positive correlation between Phy and the variables PE (r = 0.35), PVC (r = 0.69), PP (r = 0.27), PS (r = 0.29), and LI (r = 0.22). However, variables such as WW (r = -0.05) have a weak correlation, and while PVC and PE showed the most significant effect on Phy concentration, WW had the lowest effect. Furthermore, LIME (local interpretable model-agnostic explanations) and SHAP (shapley additive explanations) provided us with important results for interpreting the random forest regression (RF) and DL models' predictions, respectively. The LIME and SHAP analyses suggest that the system with more PVC has a higher predicted Phy value. For WW, the reverse is true; higher WW values result in lower Phy predictions. Researchers were given the model explainability decision tree (DT) structure to study reactants' effects on output (Phy). In conclusion, the dye industry can use microalgae to treat WW contaminated with MPs while also producing high amounts of Phy using a DL model.

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.

Bioaerogels from biomass waste: An alternative sustainable approach for wastewater treatment

The generation of municipal solid waste is projected to increase from 2.1 billion tonnes in 2023 to 3.8 billion tonnes by 2050. In 2020, the direct global cost of managing this waste was approximately USD 252 billion. When considering additional hidden costs-such as those arising from pollution, adverse health effects, and climate change due to inadequate waste disposal-the total cost escalates to USD 361 billion. Without significant improvements in waste management practices, this figure could nearly double by 2050, reaching an estimated USD 640.3 billion annually. Among municipal solid waste, biowaste accounts for roughly 44 % of the global municipal solid waste, translating to about 840 million tonnes annually. They are widely accessible and economical, offering a cost-effective alternative to traditional treatment materials. Transforming biomass waste into carbon-based materials (e.g., bioaerogels) is a sustainable practice that reduces waste and repurposes it for environmental remediation. This approach not only decreases the volume of waste directed to landfills and mitigates harmful greenhouse gas emissions from decomposition but also aligns with the principles of a circular economy. Furthermore, it supports sustainable development goals by addressing issues such as water scarcity and pollution while promoting waste valorization and resource efficiency. The unique properties of bioaerogels-including their porosity, multi-layered structure, and chemical adaptability-make them highly effective for the remediation of different water pollutants from aquatic bodies. This review article comprehensively delves into multifaceted wastewater remediation strategies -based bioaerogels such as coagulation and flocculation, advanced oxidation processes, membrane filtration, catalytic processes, water disinfection, Oil-water separation, biodegradation, and adsorption. Additionally, it examines different mechanisms of interaction such as surface adsorption, electrostatic interaction, van der Waals forces, ion exchange, surface precipitation, complexation, pore-filling, hydrophobic interactions, and π-π stacking. Moreover, it conducts an integrated techno-economic evaluation to assess their feasibility in wastewater treatment. By valorizing biomass waste, a closed-loop system can be established, where waste is transformed into valuable bioaerogels. This approach not only addresses challenges related to effluent pollution but also generates economic, environmental, and social benefits. Ultimately, the review underscores the transformative potential of bioaerogels in wastewater treatment, emphasizing their crucial role in supporting long-term environmental goals and advancing the principles of resource circularity.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Review of recent advances in utilising aquaculture wastewater for algae cultivation and microalgae-based bioproduct recovery

Aquaculture operations produce large amounts of wastewater contaminated with organic matter, nitrogenous compounds, and other emerging contaminants; when discharged into natural water bodies, it could result in ecological problems and severely threaten aquatic habitats and human health. However, using aquaculture wastewater in biorefinery systems is becoming increasingly crucial as advancements in valuable bioproduct production continue to improve economic feasibility. Research on utilising microalgae as an alternative to producing biomass and removing nutrients from aquaculture wastewater has been extensively studied over the past decades. Microalgae have the potential to use carbon dioxide (CO2) effectively and significantly reduce carbon footprint, and the harvested biomass can also be used as aquafeed. Furthermore, aquaculture wastewater enriched with phosphorus (P) is a potential resource for P recovery for the production of biofertiliser. This will reduce the P supply shortage and eliminate the environmental consequences of eutrophication. In this context, the present review aims to provide a comprehensive overview of the current state of the art in a generation, as well as the characteristics and environmental impact of aquaculture wastewater reported by the most recent research. Furthermore, the review synthesized recent developments in algal biomass cultivation using aquaculture wastewater and its utilisation as biorefinery feedstocks for producing value-added products, such as aquafeeds, bioethanol, biodiesel, biomethane, and bioenergy. This integrated process provides a sustainable method for recovering biomass and water, fully supporting the framework of a circular economy in aquaculture wastewater treatment via resource recovery.

Application and innovation of artificial intelligence models in wastewater treatment

At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.

Cutting edge technology for wastewater treatment using smart nanomaterials: recent trends and futuristic advancements

Water is a vital component of our existence. Many human activities, such as improper waste disposal from households, industries, hospitals, and synthetic processes, are major contributors to the contamination of water streams. It is the responsibility of every individual to safeguard water resources and reduce pollution. Among the various available wastewater treatment (WWT) methods, smart nanomaterials stand out for their effectiveness in pollutant removal through absorption and adsorption. This paper examines the application of valuable smart nanomaterials in treating wastewater. Various nanomaterials, including cellulose nanocrystals (CNC), cellulose nanofibrils (CNF), nanoadsorbents, nanometals, nanofilters, nanocatalysts, carbon nanotubes (CNTs), nanosilver, nanotitanium dioxide, magnetic nanoparticles, nanozero-valent metallic nanoparticles, nanocomposites, nanofibers, and quantum dots, are identified as promising candidates for WWT. These smart nanomaterials efficiently eliminate toxic substances, microplastics, nanoplastics, and polythene particulates from wastewater. Additionally, the paper discusses comparative studies on the purification efficiency of nanoscience technology versus conventional methods.

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works. In the first stage, Fenton and SPF were carried out on a laboratory scale using a Taguchi L9-type experimental design. From the statistical analysis of this design, the operating parameters: pH, time, hydrogen peroxide concentration [H2O2], and iron ferrous concentration [Fe2+] that maximize the response variables: Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and color were chosen. From these, a cascade forward neural network was implemented to establish a correlation between data from the variables to the physicochemical parameters to be measure being that a great fit of the data was obtained having a correlation coefficient of 0.99 which permits to optimize the pollutant degradation and predict the removal efficiencies at pilot scale but with a projection to a future industrial scale. A relevant result, it was found that the optimal values for maximizing the removal of physicochemical parameters were pH = 3, time = 60 min, H2O2/COD = 1.5 mg L-1, and H2O2/Fe2+ = 2.5 mg L-1. With these conditions degradation percentages of 91.44%, 47.14%, and 97.89% for COD, TOC, and color were obtained from the Fenton process, while for SPF the degradation percentage increased moderately. From the ANN analysis, the possibility to establish an intelligent system that permits to predict multiple results from operational conditions has been achieved.

Ensuring carbon neutrality via algae-based wastewater treatment systems: Progress and future perspectives

The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients-including carbon, nitrogen, and phosphorus-alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms.

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Insights into levofloxacin adsorption with machine learning models using nano-composite hydrochars

Hydrothermal carbonization was applied to taro peel wastes to produce hydrochars using a facile and environmentally friendly process. Four different entities were prepared: hydrochar (TPh), phosphoric-activated hydrochar (P-TPh), and silver@hydrochars (Ag@TPh, Ag@P-TPh). The elemental compositions of the single and composite hydrochars were confirmed by EDX. Among the produced hydrochars, the morphology of the Ag@hydrochar composites demonstrated more wrinkled structure, and Ag nanoparticles decorated the surface. The optimal experimental conditions for levofloxacin adsorption were determined to be a contact time of 45 min, hydrochar dose of 0.15 g L-1, and pH of 7. The best adsorption performances were assigned to Ag@hydrochars. Two machine learning models were applied to predict the levofloxacin adsorption efficiency of the Ag@hydrochars. A central composite design (CCD) and a 3-10-1 artificial neural network (ANN) model were developed to estimate the removal performance of levofloxacin using Levenberg-Marquardt backpropagation algorithm based on correlation and error analysis of the adopted training functions. Furthermore, the ANN sensitivity analysis revealed the order of the relative importance variable as initial concentration> hydrochar dose> pH. The predicted values of the CCD and ANN models fitted the experimental results with R2> 0.989. Therefore, the applied models were effective in predicting levofloxacin removal under different operating conditions. This work provides an open option for the sustainable management of food industry wastes and the possibility of waste valorization to effective hydrochar composites to be applied in water treatment processes.

A fluorescence based dual sensor for Zn2+ and PO43- and the application of soft computing methods to predict machine learning outcomes

A phenolphthalein-based Schiff base, 3,3-bis-{4-hydroxy-3-[(pyridine-2-ylmethylimino)-methyl]-phenyl}-3H-isobenzofuran-1-one (PAP), has been synthesized and used for selective fluorescence 'turn on' and 'turn off' sensing of Zn2+ and PO43- respectively. The limit of detection using the 3σ method for Zn2+ is found to be 19.3 nM and that for PO43- is 8.3 μM. The sensing mechanism of PAP for Zn2+ ions has been explained by 1H NMR, 13C NMR, TRPL, ESI-MS, FT-IR, and DFT based calculations. Taking advantage of this fluorescence 'on-off' behavior of PAP in the sequential presence of Zn2+ and PO43- a two input fuzzy logic (FL) operation has been constructed. The chemosensor PAP can thus act as a metal ion and anion responsive molecular switch, and its corresponding emission intensity is used to mimic numerous FL functions. To replace various expensive, time-consuming experimental procedures, we implemented machine learning soft computing tools, such as fuzzy-logic, artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS), to correlate as well as predict the fluorescence intensity in the presence of any equivalent ratio of Zn2+ and PO43-. The statistical performance measures (MSE and RMSE, for example) show that the projected values of the cation and anion sensing data by the ANFIS network are the best and closer to the experimental values.

Integrated machine learning methods with oversampling technique for regional suitability prediction of waste-to-energy incineration projects

China's tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

A comparative study of response surface methodology and artificial neural network based algorithm genetic for modeling and optimization of EP/US/GAC oxidation process in dexamethasone degradation: Application for real wastewater, electrical energy consumption

Dexamethasone (DXM) is a broadly used drug, which is frequently identified in the water environments due to its improper disposal and incomplete removal in wastewater treatment plant. The inability of conventional treatment processes of wastewater causes that researchers pay a great attention to study and develop effective wastewater treatment systems. This work deals with the study of integrated electro-peroxone/granular activated carbon (EP/US/GAC) process in the degradation of dexamethasone (DXM) from a water environment and the remediation of real pharmaceutical wastewater. Two approaches of response surface methodology based on central composite design (RSM-CCD) and artificial neural network based on algorithm genetic (ANN-GA) were employed for modeling and optimization of the process. Both the models presented significant adequacy for modeling and prediction of the process according to statistical linear and nonlinear metrics (R2 = 0.9998 and 0.9996 and RMSE = 0.2128 and 0.1784 for ANN-GA and RSM-CCD, respectively). The optimization study provided the same outcomes for both ANN-GA and RSM-CCD approaches, where approximately complete DEX oxidation was achieved at pH = 9.3, operating time = 10 min, US power = 300 W/L, applied current = 470 mA, and electrolyte concentration = 0.05 M. A synergistic study signified that the EP/US/GAC process made an 82% synergy index as compared to the individual US and EP processes. The calculated energy consumption for the integrated process was achieved to be 2.79 kW h/gCOD. Quenching test by tert-butanol and p-benzoquinone revealed that HO• radical possessed the largest contribution in DEX degradation. The efficiency of EP/US/GAC process in the remediation of real pharmaceutical wastewater showed a significant decline in COD content (92% removal after 180 min), and the ratio of initial BOD/COD ratio of 0.27 was elevated up to 0.7 after 100 min treatment time. The performance stability of EP/US/GAC system showed no remarkable drop in removal efficiency, and leakage of lead ions from the anode surface was negligible and below WHO guideline for drinking water. Generally, this research work manifested that the integrated EP/US/GAC system elevated the degradation efficiency and can be proposed as a pretreatment step before biological treatment processes for the remediation of recalcitrant wastewaters.

Application of ANN-MOGA for nutrient sequestration for wastewater remediation and production of polyunsaturated fatty acid (PUFA) by Chlorella sorokiniana MSP1

Chlorella bears excellent potential in removing nutrients from industrial wastewater and lipid production enriched with polyunsaturated fatty acids. However, due to the changing nutrient dynamics of wastewater, growth and metabolic activity of Chlorella are affected. In order to sustain microalgal growth in wastewater with concomitant production of PUFA rich lipids, RSM (Response Surface Methodology) followed by heuristic hybrid computation model ANN-MOGA (Artificial Neural Network- Multi-Objective Genetic Algorithm) were implemented. Preliminary experiments conducted taking one factor at a time and design matrix of RSM with process variables viz. Sodium chloride (1 mM-40 mM), Magnesium sulphate (100 mg-800 mg) and incubation time (4th day to 20th day) were validated by ANN-MOGA. The study reported improved biomass and lipid yield by 54.25% and 12.76%, along with total nitrogen and phosphorus removal by 21.92% and 18.72% respectively using ANN-MOGA. It was evident from FAME results that there was a significantly improved concentration of linoleic acid (19.1%) and γ-linolenic acid (21.1%). Improved PUFA content makes it a potential feedstock with application in cosmeceutical, pharmaceutical and nutraceutical industry. The study further proves that C. sorokiniana MSP1 mediated industrial wastewater treatment with PUFA production is an effective way in providing environmental benefits along with value addition. Moreover, ANN-MOGA is a relevant tool that could control microalgal growth in wastewater.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Recent applications of AI to environmental disciplines: A review

The rapid development and efficiency of Artificial Intelligence (AI) tools have made them increasingly popular in various fields and research domains. The environmental discipline is now experiencing an exponential interest in harnessing the potential of AI over the past decade. We have reviewed the latest applications of AI tools in the environmental disciplines, highlighting the opportunities they present and discussing their advantages and disadvantages in this field. After the emergence of deep learning algorithms in 2010, interest in using AI tools for environmental tasks has grown exponentially. Among the studied articles, over 65 % of environmental tasks that demonstrate interest in using AI tools initially relied on conventional statistical and mathematical models. Using AI tools can greatly benefit the areas of environmental science and engineering. One of the main advantages of utilizing AI tools is their ability to analyze and process large amounts of data efficiently. Recently, the European Union established a European supercomputing ecosystem program to advance science and enhance the quality of life for its citizens. Nine of these projects prioritize environmental and sustainable goals. Despite the benefits of AI, it is still in its early stages of development, which comes with environmental concerns. The amount of power consumed and the time required to train an AI model can greatly affect the carbon emissions it produces, exacerbating the challenges posed by climate change. Efforts are currently underway to develop AI technology that is environmentally sustainable, minimizes energy consumption, and has a low carbon footprint. Selecting the appropriate AI model architecture can reduce energy consumption by almost 90 %. The main finding suggests that collaboration between environmental and AI professionals becomes crucial in leveraging the full potential of AI in addressing pressing environmental challenges.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Automatic Identification of Individual Nanoplastics by Raman Spectroscopy Based on Machine Learning

The increasing prevalence of nanoplastics in the environment underscores the need for effective detection and monitoring techniques. Current methods mainly focus on microplastics, while accurate identification of nanoplastics is challenging due to their small size and complex composition. In this work, we combined highly reflective substrates and machine learning to accurately identify nanoplastics using Raman spectroscopy. Our approach established Raman spectroscopy data sets of nanoplastics, incorporated peak extraction and retention data processing, and constructed a random forest model that achieved an average accuracy of 98.8% in identifying nanoplastics. We validated our method with tap water spiked samples, achieving over 97% identification accuracy, and demonstrated the applicability of our algorithm to real-world environmental samples through experiments on rainwater, detecting nanoscale polystyrene (PS) and polyvinyl chloride (PVC). Despite the challenges of processing low-quality nanoplastic Raman spectra and complex environmental samples, our study demonstrated the potential of using random forests to identify and distinguish nanoplastics from other environmental particles. Our results suggest that the combination of Raman spectroscopy and machine learning holds promise for developing effective nanoplastic particle detection and monitoring strategies.

Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence

In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.

Production of chemicals and utilities in-house improves the environmental sustainability of phytoplankton-based biorefinery

Life cycle assessment was used to evaluate the environmental impacts of phytoplanktonic biofuels as possible sustainable alternatives to fossil fuels. Three scenarios were examined for converting planktonic biomass into higher-value commodities and energy streams using the alga Scenedesmus sp. and the cyanobacterium Arthrospira sp. as the species of interest. The first scenario (Sc-1) involved the production of biodiesel and glycerol from the planktonic biomass. In the second scenario (Sc-2), biodiesel and glycerol were generated from the planktonic biomass, and biogas was produced from the residual biomass. The process also involved using a catalyst derived from snail shells for biodiesel production. The third scenario (Sc-3) was similar to Sc-2 but converted CO2 from the biogas upgrading to methanol, which was then used in synthesizing biodiesel. The results indicated that Sc-2 and Sc-3 had a reduced potential (up to 60 % less) for damaging human health compared to Sc-1. Sc-2 and Sc-3 had up to 61 % less environmental impact than Sc-1. Sc-2 and Sc-3 reduced the total cumulative exergy demand by up to 44 % compared to Sc-1. In conclusion, producing chemicals and utilities within the biorefinery could significantly improve environmental sustainability, reduce waste, and diversify revenue streams.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Physiological and transcriptome profiling of Chlorella sorokiniana: A study on azo dye wastewater decolorization

Over decades, synthetic dyes have become increasingly dominated by azo dyes posing a significant environmental risk due to their toxicity. Microalgae-based systems may offer an alternative for treatment of azo dye effluents to conventional physical-chemical methods. Here, microalgae were tested to decolorize industrial azo dye wastewater (ADW). Chlorella sorokiniana showed the highest decolorization efficiency in a preliminary screening test. Subsequently, the optimization of the experimental design resulted in 70% decolorization in a photobioreactor. Tolerance of this strain was evidenced using multiple approaches (growth and chlorophyll content assays, scanning electron microscopy (SEM), and antioxidant level measurements). Raman microspectroscopy was employed for the quantification of ADW-specific compounds accumulated by the microalgal biomass. Finally, RNA-seq revealed the transcriptome profile of C. sorokiniana exposed to ADW for 72 h. Activated DNA repair and primary metabolism provided sufficient energy for microalgal growth to overcome the adverse toxic conditions. Furthermore, several transporter genes, oxidoreductases-, and glycosyltransferases-encoding genes were upregulated to effectively sequestrate and detoxify the ADW. This work demonstrates the potential utilization of C. sorokiniana as a tolerant strain for industrial wastewater treatment, emphasizing the regulation of its molecular mechanisms to cope with unfavorable growth conditions.