Abstract This paper explores the current landscape of bioplastics, focusing on the utilization of agri‐food waste (AFW) as a valuable resource for their production using Artificial Intelligence (AI) and machine learning (ML) techniques. The processing methodology for converting such waste into bioplastics is elaborated, encompassing various innovative techniques and technologies enhanced by AI‐driven optimization. Specific examples of agri‐food waste utilized for bioplastic production, including bagasse, beer spent grain, tomato pomace, olive pomace, and rice husk, are included, showcasing the diverse range of feedstocks available for sustainable manufacturing practices. AI applications span multiple stages of the AFW‐to‐bioplastic value chain, including feedstock characterization via machine learning‐based composition analysis, fermentation process control using reinforcement learning and neural networks, property prediction through ensemble tree‐based models, and formulation design using genetic algorithms and generative adversarial networks. The potential applications of bioplastics across a multitude of sectors, including packaging, agriculture, textiles, 3D printing, medical, consumer goods, and electronics, are thoroughly examined. The industrial potential of utilizing agri‐food waste for bioplastic production is elucidated through techno‐economic assessments and digital twin technologies. From a circular bioeconomy perspective, it is essential to consider the importance of waste prevention, resource valorization, sustainable production practices, consumption patterns, and waste management strategies in the bioplastics industry, all of which are increasingly optimized using AI‐driven tools. Finally, this work addresses the challenges and future directions in the field of bioplastics, identifying areas for further research and development, including the standardization of AI models and the scaling of intelligent manufacturing systems. By highlighting the untapped potential of agri‐food waste for bioplastic production and the transformative role of artificial intelligence throughout the value chain, this review aims to contribute to the advancement of sustainable, data‐driven, and resource‐efficient solutions in the plastics industry. © 2026 Society of Chemical Industry (SCI).

The global decline in access to safe and potable water is a growing social concern with increasing relevance projected to escalate in the coming decades. Different techniques for purifying water have been developed and tested to address this issue. More recently, there has been a technological trend in adopting carbon nanotube (CNT) technology for wastewater treatment. However, due to the immature state of research in the area, several aspects remain. In response to these challenges, this study conducts a bibliometric analysis to assess the current knowledge regarding the utilization and advancement of CNTs in wastewater treatment. The study utilised various software applications to map the relevant literature published between 2013 and 2023, encompassing 699 scholarly articles that underwent content analysis. The findings revealed how the knowledge on the topic is organized, highlighting key collaboration patterns among authors, institutions, and journals. They also identified the most influential authors, providing valuable insights into the networks and dynamics that shape research progress in this area. The research findings also reveal the primary technological trends in utilizing CNTs for water treatment and the challenges that hinder their practical application. To guide advancements in this field, the article contributes to indicating future research avenues systematically organized into three critical categories: (i) investigations targeting the most frequently identified literature gaps, (ii) studies focusing on process optimization and operational efficiencies, and (iii) exploration of emerging technologies and evolving trends. This structured approach provides a clear roadmap for overcoming existing barriers and unlocking the full potential of CNTs in wastewater treatment applications. This article can serve as a reference for subsequent technological investigations.

Abstract: Hydrogen blending into natural gas pipelines is a promising strategy for decarbonizing energy systems while utilizing existing infrastructure. Current global research and industrial standards recommend blending ratios of 5–20% by volume, as this range balances meaningful emission reductions with material compatibility constraints of existing pipelines and end-use appliances. A blending ratio of up to 20% is adopted as the research boundary in this study, representing the upper threshold endorsed by regulatory bodies and pilot programs worldwide and providing specific physical boundary conditions for discussing challenges such as hydrogen diffusion behavior, explosion risk thresholds, and compressor efficiency decline. However, hydrogen blending introduces technical challenges, including material embrittlement, increased leakage risk, and reduced operational efficiency. This review examines how Artificial Intelligence (AI) and Machine Learning (ML) can address these challenges in hydrogen-blended natural gas pipeline systems. Predictive analytics, physics-informed ML, and digital twins improve flow modeling, material behavior prediction, and fault detection. AI-controlled optimization manages hydrogen injection, compressor control, and blending ratios to maintain a consistent energy supply. AI-enabled sensors detect anomalies in real time, accounting for hydrogen's flammability and high diffusivity, thereby enhancing safety. Supply chain efficiency, cost reduction, and emissions minimization are further supported through AIdriven real-time process management. Despite these advances, challenges in data availability, model generalisation, cybersecurity, and interpretability remain significant barriers. Overcoming them requires interdisciplinary collaboration and the development of Explainable AI (XAI) and edge computing frameworks. This review demonstrates that AI can accelerate the transition to safe, dependable, and affordable hydrogen energy systems.

Transitioning from fossil fuels to non-carbon fuels is essential in pursuing carbon neutrality. A gradual combination of natural gas and hydrogen could offer a smooth shift and reduce potential energy distribution and heating source disruptions. Academic institutions, industry, and governments worldwide actively support hydrogen mixing projects such as “HyDeploy”, “GRHYD”, “THyGA”, and Hy Blend to develop effective pathways to reduce carbon emissions. To successfully commercialize hydrogen blending, it is crucial to make scientific advancements and conduct a favorable techno-economic analysis. Current studies are centered around gaining a deeper understanding of methane-hydrogen mixtures. Researchers are examining various properties of these mixtures, including “density, phase interactions, viscosity, and energy densities”. The goal is to comprehend better how these properties affect massive transportation through pipeline networks and their uses in various industries. It is crucial to balance safety concerns, such as overpressure and leakage in pipelines, and hydrogen’s non-carbon energy carrier advantages. Techno-economic models are now being developed to gain insights into energy transportation efficiency and accurately estimate the cost of delivering hydrogen-blended natural gas as we transition to cleaner energy systems. This review highlights significant global initiatives to boost confidence in the shift toward methane-hydrogen gas blends, which serve as a stepping stone toward the widespread implementation of a hydrogen-based economy by 2050.

Membrane-based separation processes have been gaining significant attention in the treatment of oily wastewater. To date, a remarkable amount of data is available on the application of membranes in diverse domains such as industrial wastewater treatment, food processing, and medicine. It is becoming a severe issue when process sectors like mining, metallurgy, and petrochemicals discharge oily wastewater. Although oil–water emulsion separation using membrane technology is successful, this method suffers from a serious flux declination problem brought on by fouling during filtration. Keeping this in mind, the aim of this paper is to highlight the recent advancements in the synthesis of ceramic membranes from several perspectives such as feed pretreatment, membrane cleaning, proper operational conditions, and the use of antifouling coatings. Recent study has indicated that surface hydrophilization is the key emphasis in mitigating membrane fouling. Thus, the current state of membrane surface modification technology is reviewed, and future trends are identified.

The study develops a stochastic optimisation model to minimise compressor fuel consumption using the hydrodynamic principles of pipeline and compressor facilities. The model was operationalised on a Natural Gas Pipeline Network (NGPN) and optimised using an Improved Ant Colony Optimization (IACO) methodology. The results of IACO and three other popular optimisation methodologies - Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Generalized Reduced Gradient (GRG) - were compared. Results indicated a significant decrease of 27.9% in fuel consumption when IACO was compared to the base scenario (fuel consumption: 0.86). The results were significantly better than those obtained using other techniques. The study also analyses the results on multi-objective scales using bi-, tri-, and tetra-objective scales. The primary contribution of this study is reporting the optimal solution in each multi-objective research, an element that most previous researchers have not reported.

Hydrogen is universally recognized as a highly viable and sustainable energy carrier. However, transporting hydrogen to customers is a significant challenge. A cost-effective method for transporting hydrogen on a larger scale involves blending it with natural gas (NG). Nevertheless, there is uncertainty regarding the maximum quantity of hydrogen that can be transported by blending it with natural gas (NG) while adhering to practical constraints. Owing to the limitations of the maximum operating pressure and velocity constraints, each natural gas pipeline network (NGPN) has a different proportion of hydrogen that can be blended. In this scenario, the paper presents a novel computational optimization model to enhance the operational plan of the hydrogen blend natural gas pipeline network (HB-NGPN). The model’s primary objective is to manage hydrogen blending percentages in the natural gas pipeline network (NGPN). The model comprehensively articulates the fundamental principles governing the hydraulics of gas pipelines and compressor stations, facilitating the simulation of the HB-NGPN. An NGPN, initially intended to transport natural gas (NG), was used to implement the developed model. An evolutionary algorithm, ‘improved ant colony optimization (IACO),’ optimizes the HB-NGPN. The operational variables were investigated and compared with previously published work to determine how varying the quantities of hydrogen affect them. Our results show an increase in permissible hydrogen content (10.40

The field of artificial intelligence (AI) is advancing at a rapid pace. The evolving technology can replicate practical scenarios in different industries, such as water purification and wastewater treatment. It has demonstrated its value in agriculture, the automotive industry, banking, finance, space exploration, and creative technology. Wastewater treatment has seen significant improvements with the integration of artificial intelligence, which enhances efficiency, speed, and autonomy. Nevertheless, there are notable obstacles and limitations to address, such as data management challenges, interpretability concerns, model replicability and consistency issues, and the need for scholarly transparency. Improving model causality, clarity, data management, and repeatability are proposed solutions. The chapter introduced some of the recent artificial intelligence based optimization methodologies to optimize waste water treatment plants.

The extensive reliance on conventional fossil fuels escalates greenhouse gas emissions and exacerbates environmental pollution, necessitating an urgent energy shift. Consequently, the advancement and use of renewable energy are of paramount significance. Hydrogen is expected to progress in the following years as a replacement for fossil fuels among various renewable sources. However, the high cost of the hydrogen supply network (HSN) has provoked the researchers to use optimization techniques to optimize the (HSN). The present study analyzes modern optimization methods for (HSN) and thoroughly examines the 'Green hydrogen supply chain'(GHSC), including transportation, production, storage and consumption, emphasizing metaheuristic optimization (MO) applications. The difficulties inherent in each phase are highlighted, and the capacity of MO techniques to mitigate these difficulties is examined. The study also examines multi-objective methodologies for optimizing issues within this area.

Air separation processes are time-consuming and energy-intensive. Most of the energy used in air separation unit (ASU) is used for air compression. During the air compression process, some energy is lost, which is converted into waste heat. This wasted energy is used to warm liquefied natural gas (LNG). At some point, LNG ships will dock at an LNG regasification facility. Here, LNG is converted back to gas and supplied to the distribution and transmission systems. During the regasification process, cryogenic LNG has a huge opportunity for cold energy recovery. An innovative air separation process that is integrated with the cold utilization of LNG is presented in this study along with a thorough conceptual design and analysis. The results of this study show that producing high-purity oxygen and nitrogen, respectively, requires 0.28 kWh kg-1 and 0.06 kWh kg-1 of specific energies. Prior to integration with cold utilization of natural gas, 25 141.6 kW is needed for air compression. However, following integration, 10 554.6 kW of energy is needed, resulting in a 58.01 % energy savings. Exergy destruction as well as efficiency have been calculated for the primary components of the system. Sensitivity analysis is carried out to examine the effects of LNG streams on important parameters. In conclusion, a cryogenic ASU is integrated with an LNG-direct expansion cycle-organic Rankine cycle power cycle to supply the necessary power for operation and reduce extraneous power inputs. Overall, this integrated approach increases efficiency, lowers costs, benefits the environment, allows for flexibility and adaptability, and raises system dependability. An innovative approach integrates cryogenic air separation with liquefied natural gas (LNG) cold utilization, optimizing energy efficiency and reliability. By leveraging waste heat from air compression to warm LNG during regasification and power generation, the proposed system achieves a remarkable 58.01 % energy savings. The synergistic design enhances the production of high-purity oxygen and nitrogen, presenting a sustainable solution that reduces costs, benefits the environment, and enhances overall system dependability. image

The viability of biomass to be used as a consumer product relies heavily on the cost of a Biomass supply network (BSN) that links biomass producers with biorefineries and, finally, with end customers. The current study aims to establish a cost optimization model to minimize the financial burden of BSN. A MILP model has been established and implemented to reduce the costs of a BSN. A comparatively lesser-used stochastic technique, Ant Colony Optimization (ACO), has been used in the present paper to minimize the cost of BSN. Although the ACO technique has succeeded in other settings, it is seldom tested in the context of BSN. The results from the ACO approach have been compared with another popular stochastic optimization technique called the Non-sorting Genetic Algorithm (NSGA-II). According to empirical research, the ACO approach is the most cost-effective optimization technique to lower BSN-related costs. The management may use the blueprint of the optimization model and techniques to develop cost-cutting measures for BSN.

The increasing recognition of environmental deterioration has positioned microalgae as a potential solution for mitigating greenhouse gases and producing chemicals. However, challenges related to the cost and productivity of microalgae cultivation hinder the scaling up of production techniques for industrial and domestic purposes. Enhancing resource use efficiency can lower production costs and enhance the sustainability of microalgae products. Utilizing wastewater as an economical nutrient source holds promise. Microalgae cultures also exhibit the ability to remove pollutants, suggesting their potential for tertiary and quaternary water treatment, especially in water-scarce conditions. This study examines microalgae-based solutions for wastewater treatment, addressing environmental pollution and resource recovery.

The sustainable valorization of agricultural biomass waste is gaining momentum as an effective waste management strategy. Pomegranate (Punica granatum) and sweet lemon (Citrus limetta) peels, abundant by-products from the fruit processing industry, are currently underutilized and discarded as waste. This study aims to assess the suitability of pomegranate and sweet lemon peels for sustainable biomass valorization through a comprehensive characterization study. Biomass waste is processed and subsequently, various characterization techniques are applied to unveil its intrinsic properties. These methods encompass Field Emission Scanning Electron Microscope (FESEM), Energy-dispersive X-ray spectroscopy (EDS), Differential Scanning Calorimetry (DSC) and Fourier Transform Infrared Spectroscopy (FTIR). Elemental analysis reveals the predominance of carbon and oxygen in both peel types. Pomegranate peels and sweet lemon peels exhibit carbon content of 56.43

In order to stop the spread of foodborne infections, which are considered to be major hazards to human health, rapid identification of foodborne pathogens at their earliest stages is essential. Traditional bacterial culture techniques for detecting foodborne pathogens are time-consuming, labor-intensive, and have subpar pathogen identification skills. This has led researchers to challenge the effectiveness of present detection methods and use new technology to improve pathogen sensing results. For online pathogen monitoring with high precision and specificity while saving time and money, novel solutions primarily focus on combining all of the procedures from preparing the samples to detection in miniaturized sensors. Biosensors are widely used in many fields, including biochemistry, electrochemistry, agriculture, and biomedicine. They may incorporate several point-of-care (POC) programs, including those in the food, medical, environmental, forensic, pharmaceutical, and biological industries. Microfluidic biosensors offer advantages such as enhanced mobility, increased operational transparency, improved manageability, and increased consistency due to their utilization of a small reaction volume for sensing. Due to the aforementioned factors, this technique is a prime choice for the creation of devices like microfluidic biosensors. Microfluidic chip architecture and multiplexed detection technologies provide a fresh approach to achieving this objective. For the multiplex detection of foodborne pathogens, we proposed several preparation techniques and associated detection methods using microfluidic devices in this study. This chapter addresses the design, categorization, developments, and difficulties in microfluidic-based biosensors. Miniature microfluidic-based biosensor devices, encompassing manufacture and related methods, are critically examined for their potential use in a broad variety of POC diagnostic applications.

The effectiveness of low-cost fly ash geopolymer (FAGP) adsorbents synthesized from biomass fly ash in lead removal from aqueous solution was studied. The synthesized FAGP was characterized by the Brunauer-Emmett-Teller method and energy dispersive spectroscopy. The adsorption experiments were performed in batch mode under various conditions, and the maximum removal efficiency and uptake were found at an optimum time of 120 min and pH 5. Adsorption isotherm studies confirmed that lead removal is best fitted by both Langmuir and Freundlich isotherms. A kinetic analysis showed that pseudo-second-order kinetics governs lead adsorption. Lead adsorption was determined to be an endothermic, spontaneous process through thermodynamic analysis. Adsorption is an effective method for removing heavy metals from water. Herein, a geopolymer synthesized from biomass fly ash was tested as an adsorbent for removing Pb from solution. The effects of contact time, pH, adsorbent dosage, and initial Pb concentration were tested. The results showed that this is a viable option for removing Pb from solution and reducing the accumulation of biomass fly ash. image

The paper develops a statistical model for optimizing the Hydrogen-injected Natural gas (H-NG) high-pressure pipeline network. Gas hydrodynamic principles are utilized to construct the pipeline and compressor station model. The model developed is implemented on a pipeline grid that is supposed to carry Hydrogen as an energy carrier in a natural gas-carrying pipeline. The paper aims to optimize different objectives using ant colony optimization (ACO). The first objective includes a single objective optimization problem that evaluates the maximum permissible hydrogen amounts blended with natural gas (NG) for a set of pipeline constraints. We also evaluated the variations in operational variables on injecting Hydrogen into the natural gas pipeline networks at varying fractions. The study further develops a multi-objective optimization model that includes bi-objective and tri-objective problems and is optimized using ACO. Traditional studies have focused on single-objective optimization with minimal bi-objective issues. In addition, none of the earlier research has shown the effect of introducing Hydrogen to the NG network using tri-objective function evaluations. The bi-objective and tri-objective functions help evaluate the effect of injecting Hydrogen on different operational parameters. The study further attempts to fill the gap by detailing the modelling equations implemented through a bi-objective and tri-objective function for the H-NG pipeline network and optimized through ACO. Pareto fronts that show the tradeoff between the different objectives for the multi-objective problem have been generated. The primary objective of the bi-objective and tri-objective optimization problems is maximizing hydrogen mole percent in natural gas. The other objective chosen is minimizing compressor fuel consumption and maximizing delivery pressure, throughput, and power delivered at the delivery station. The findings will serve as a roadmap for pipeline operators interested in repurposing natural gas pipeline networks to transport hydrogen and natural gas blend (H-NG) and seeking to reduce carbon intensity per unit of energy-delivered fuel.

Developing effective optimization models to improve pipeline network profitability in oil and gas supply chains is one of the most promising research areas in this industry. Because of the substantial advantages natural gas networks’ operations have realized, this industry has become more competitive and eager to develop robust supply optimization decision models. However, although several models and techniques have been developed to reduce natural gas consumption, only very few studies have focused on comparing the performance of these models and the implications of the distinct optimization performances. Consequently, the generalizability of the research in the area is still problematic, representing a research area not sufficiently explored. Taking this into account, this paper compares the fuel consumption values in a French gas pipeline by analyzing the Genetic algorithms (GA), Generalized reduced gradient (GRG), and Ant colony optimization (ACO) models. Overall, our findings show significant differences in gas consumption when the ACO and GA are compared with the GRG technique. Furthermore, the findings indicate that ACOs are competitive with GA and GRG in computational efficiency in finding near-global optimized solutions. The article can assist decision-makers and policymakers in discovering the most profitable operational parameters to minimize gas consumption and increase the profitability of natural gas networks.