Industrial Usage Research Articles

ITRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its ϵ-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

A Study on The Relationship Between Digital Competencies, Mindfulness, Social Media Usage and Work Performance in Automobile Industry of Pakistan

This study aims to investigate the relationship between mindfulness, digital competencies, social media usage, and work performance in Pakistan's automobile sector. The research explores the roles of trust, mindfulness, shared vision, digital competency, knowledge transfer, and network ties within this industry. The automobile industry demands high levels of employee performance, and social media networks are increasingly used for communication and interaction. This research hypothesizes that social media usage fosters trust among employees, leading to better interaction and knowledge transfer. Additionally, mindfulness training is proposed to enhance work performance and reduce employee turnover. A quantitative approach with a conceptual framework will be used to test these hypotheses. The findings will contribute to a better understanding of how these factors influence work performance in the Pakistani automobile industry.

Natural Gas as an Alternative to Fuel Oil Usage in the Steel Industry

Steel industry is a major energy consumption sector requiring large quantities of fuel in order to operate its various units. There are two main types of fuel used in the steel industry, fuel oil and natural gas. Natural gas burns better than fuel oil and produces less combustion products that are harmful to the environment. The Libyan Iron and Steel Company uses fuel oil as a main source of energy in its operations. Calculations are carried out to determine equivalent amounts of natural gas to fuel oil being used in the company as well as quantities of CO2, SO2 produced from combustion of both fuels. The results showed the possibility of reducing the amounts CO2 by about 25.7% and eliminating SO2 by switching to natural gas usage in the company.

Natural Gas as an Alternative to Fuel Oil Usage in the Steel Industry

Steel industry is a major energy consumption sector requiring large quantities of fuel in order to operate its various units. There are two main types of fuel used in the steel industry, fuel oil and natural gas. Natural gas burns better than fuel oil and produces less combustion products that are harmful to the environment. The Libyan Iron and Steel Company uses fuel oil as a main source of energy in its operations. Calculations are carried out to determine equivalent amounts of natural gas to fuel oil being used in the company as well as quantities of CO2, SO2 produced from combustion of both fuels. The results showed the possibility of reducing the amounts CO2 by about 25.7% and eliminating SO2 by switching to natural gas usage in the company.

Microbial osmotic pressure desalination system for desulfurization wastewater from coal-fired power plants: A pilot study

The treatment of desulfurization wastewater has emerged as a critical area of research due to the significant water consumption and drainage volume associated with thermal power plants, which account for 30 %–40 % of industrial water usage. The desalination and treatment of this wastewater generate desulfurization byproducts, posing environmental risks and incurring high operational costs. This study established a pilot-scale desalination system based on the principle of microbial osmotic pressure for the treatment of desulfurization wastewater from power plants. The desulfurization wastewater primarily contains Cl−, SO42−, Mg2+, Ca2+, and Na+ ions. The microbial osmotic pressure device reduced the water conductivity from 26,000 μs/cm to below 8000 μs/cm, achieving a total salt removal rate of 70 %. The removal rates for Cl−, SO42−, Mg2+, Ca2+ and Na+ were 69.10 %, 76.55 %, 73.0 %, 35.34 % and 52.80 %, respectively. The desalinated water from the osmotic pressure device then undergoes aerobic removal of organic compounds, followed by membrane filtration and reverse osmosis for further desalination. The effluent conductivity is <1000 μs/cm, meeting the standards for reuse water. The microbial osmotic pressure device enriched salt-absorbing halobacteria and was dominated by norank_f__Bacteroidetes_vadinHA17, Trichococcus, Acidovorax, Soehngenia, and norank_f__Hungateiclostridiaceae. Compared to traditional processes, the biological desalination process demonstrates lower operational and annual maintenance costs under conditions of regular addition of microbial agents and liquid activators to sustain the desalination bacterial community. Future research could focus on desalination microbial cultivation methods to improve stability and reduce costs.

Optimizing the integration of renewable energy sources, energy efficiency, and flexibility solutions in a multi-network pharmaceutical industry

In the contemporary landscape, roughly one-fourth of worldwide carbon dioxide emissions stem from industrial energy usage. In the industrial sector, improving the efficient and flexible coupling among different energy demands (electricity, heating, and cooling) and exploiting the integration of Renewable Energy Sources (RESs) and waste heat can lead to a drastic reduction in CO2 emissions, these are also the goals of the EU founded Horizon Europe FLEXIndustries project. This study aims to establish a cost-optimized decarbonization strategy for an energy-intensive industry, focusing on an Italian pharmaceutical company. It delves into the exploration of potential pathways and diverse energy mix configurations. The approach undertaken involves coupling a customized energy system simulation framework, specifically designed for the industrial site, with a Multi-Objective Evolutionary Algorithm (MOEA). The study, conducted with a focus on the year 2024, involves a comparative analysis of three distinct scenarios. Within the intricate and challenging constraints of the industrial demo site, 13 technologies were investigated. The outcomes of each scenario identify a set of 500 Pareto optimal solutions, obtained through 40,000 simulations. These results shed light on the compelling potential of hybrid solutions, showcasing the feasibility of achieving substantial decarbonization with only moderate increases in costs. The availability of land for RES technologies, along with the existence of a biomass supply chain in the region, emerge as pivotal determinants.

Design and Construction of an Anaerobic Digester for the Ingestion of Waste from the Cocoa Industry in Nigeria

The Anaerobic Digester designed and constructed for waste ingestion from the Nigerian cocoa industry aims to harness abundant renewable energy from crop residues like cocoa rinds and groundnuts. This study evaluates the biogas potential of these sources in contributing to the country&amp;apos;s overall energy needs, emphasizing the imperative for environmental sustainability. Focusing on reducing fossil energy consumption, greenhouse gas emissions, and minimizing environmental impact, the project advocates for a shift towards biogas for day-to-day energy requirements, presenting direct cost savings. The utilization of fossil fuel-derived energy is known to contribute to temperature increase, greenhouse gas emissions, noise pollution, and ground-level air pollution, all of which can be mitigated through biogas utilization. This initiative involves the design and construction of a 0.24m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; pilot plastic fossil plant for biogas generation, aiming to &amp;quot;green&amp;quot; various applications, including domestic and industrial usage as well as transportation. The digester, constructed from high-density polyethylene (HDPE) plastic, demonstrates leak-free operation, further supporting its potential for long-term sustainability. Results from a 28-day retention period show a cumulative biogas yield, with a daily assessment indicating a gas yield of 0.0496 m&amp;lt;sup&amp;gt;3&amp;lt;/sup&amp;gt; on the 12th day. The study highlights the positive and negative influences of temperature inequality gradients ≥34°C≤38°C on biogas production. This comprehensive research contributes valuable insights for the sustainable management of waste and the utilization of biogas as a viable alternative energy source.

Investigating Artificial Intelligence usage in Industries in Ghana, Case Study of Industries in Industrial Area, Accra-Ghana

Investigating Artificial Intelligence usage in Industries in Ghana, Case Study of Industries in Industrial Area, Accra-Ghana

Exploring the properties of Garcinia indica Linn rind powder and its possible usage in food industry

SummaryThe study investigates, the potential utilisation of the Garcinia indica linn rind powder as a valuable and healthy ingredient for food industry. Techno‐functional properties revealed the functional attributes which is crucial for product development. Characterisation of the powder and extract revealed the physical, chemical, and structural properties through FTIR, HPLC, and SEM. Therefore, moisture content (5.45%), ash content (2.80%), fibre content (7.56%), and fat content (9.54%) were reported in G. indica linn rind. Phytochemical analysis indicated significant levels of phenolic and flavonoid compounds, suggesting antioxidant potential, supported by inhibition in the DPPH assay with IC50 value of 485.1 μg/mL. In‐vitro assays revealed inhibitory effects on α‐amylase with IC50 value of 58.43 μg/mL and glucose with an IC50 value of 0.39 μg/mL, indicating potential benefits in blood glucose management and weight control. Overall, G. indica rind powder shows promise as a natural ingredient with diverse applications in functional foods and nutraceuticals, offering potential health benefits through innovative product formulations.

Research on the quantitative relationship of the viscosity reduction effect of large-ring cyclodextrin on potato starch during gelatinization process and mechanism analysis

Starch is extensively used across various fields due to its renewable properties and cost-effectiveness. Nonetheless, the high viscosity that arises from gelatinization poses challenges in the industrial usage of starch at high concentrations. Thus, it's crucial to explore techniques to lower the viscosity during gelatinization. In this study, large-ring cyclodextrins (LR-CDs) were synthesized from potato starch (PS) by using 4-α-glucanotransferase and then added to PS to alleviate the increased viscosity during gelatinization. The results from rapid viscosity analyzer (RVA) demonstrated that the inclusion of 5 % (w/w) LR-CDs markedly reduced the peak viscosity (PV) and final viscosity (FV) of PS by 49.85 % and 28.17 %. In addition, there was a quantitative relationship between PV and LR-CDs. The equation was fitted as y = 2530.73×e−x/2.48+1832.79, which provided a basis for the regulation of PS viscosity. The mechanism of LR-CDs reducing the viscosity of PS was also studied. The results showed that the addition of LR-CDs inhibited the gelatinization of PS by enhancing orderliness and limiting water absorption, resulting in a decrease in viscosity. This study provides a novel method for reducing the viscosity of starch, which is helpful for increasing its concentration and reducing energy consumption in industrial applications.

Quantitative analysis of the electromagnetic hybrid nanofluid flow within the gap of two tubes using deep learning neural networks

Purpose(1) A mathematical model for the Hybrid nanofluids flow is used as carriers for delivering drugs. (2) The flow conditions are controlled to enable drug-loaded nanofluids to flow through the smaller gap between the two tubes. (3) Hybrid nanofluids (HNFs) made from silver (Ag) and titanium dioxide (TiO2) nanoparticles are analyzed for applications of drug delivery. (Ag) and (TiO2) (NPs) are suitable candidates for cancer treatment due to their excellent biocompatibility, high photoactivity, and low toxicity. (4) The new strategy of artificial neural networks (ANN) is used which is machine-based and more prominent in validation, and comparison with other techniques.Design/methodology/approachThe two Tubes are settled in such a manner that the gap between them is uniform. The Control Volume Finite Element Method; Rk-4 and Artificial Neural Network (ANN).Findings(1) From the obtained results it is observed that the dispersion and distribution of drug-loaded nanoparticles within the body will be improved by the convective motion caused by hybrid nanofluids. The effectiveness and uniformity of drug delivery to target tissues or organs is improved based on the uniform flow and uniform gap. (2) The targeting efficiency of nanofluids is further improved with the addition of the magnetic field. (3) The size of the cylinders, and flow rate, are considered uniform to optimize the drug delivery.Research limitations/implications(1)The flow phenomena is considered laminar, one can use the same idea through a turbulent flow case. (2) The gap is considered uniform and will be interesting if someone extends the idea as non-uniform.Practical implications(1) To deliver drugs to the targeted area, a suitable mathematical model is required. (2) The analysis of hybrid nanofluids (HNFs) derived from silver (Ag) and titanium dioxide (TiO2) nanoparticles is conducted for the purpose of drug delivery. The biocompatibility, high photoactivity, and low toxicity of (Ag) and (TiO2) (NPs) make them ideal candidates for cancer treatment. (3) Machine-based artificial neural networks (ANN) have a new strategy that is more prominent in validation compared to other techniques.Social implicationsThe drug delivery model is a useful strategy for new researchers. (1) They can extend this idea using a non-uniform gap. (2) The flow is considered uniform, the new researchers can extend the idea using a turbulent case. (3) Other hybrid nanofluids flow, in the same model for other industrial usages are possible.Originality/valueAll the obtained results are new. The experimental thermophysical results are used from the existing literature and references are provided.

Heat radiation absorption and irreversibility of electromagnetic Williamson hybridized Al2O3–CoFe2O4/H2O nanofluid: A concentrated power generation

The quest for alternative and ecosystem-friendly energy sources to reduce reliance on fossil fuels and minimize cost has stimulated studies on various energy-generating techniques. Solar energy is one of the renewable and low-cost power generation sources for domestic and industrial usage. To optimize the performance of solar power, nanofluids can be considered as the based material. This study focuses on the concentrated power generation through hybridization of electromagnetic Williamson aluminium oxide (Al2O3) and cobalt ferrite (CoFe2O4) nanofluids in water (H2O) with nonlinear radiation. Without the deformation of the material, the fluid is driven by nonlinear thermal convection and pressure gradient in a bounded device. The developed, transformed boundary value mathematical model is solved by the Chebyshev scheme coupled with the collocation integrating method. A theoretical analysis is carried out for the dependent-parameter sensitivities on the flow rate, heat distribution and entropy generation to enhance and maintain continuous power generation. The investigation revealed that the hybridized nanofluid enhanced the concentrated thermal power for about 12.23 %, and the fluid velocity field is raised by 3.45 % with increasing dependent parameters. The Williamson material term optimized the thermodynamic equilibrium of entropy generation.

Chemometrical methods applied to chemical data of raw and cooked spinach (Chenopodium vulvaria) and purslane (Potulacae oleracea)

Hundreds of edible wild plants form a vegetal environment that provides a supply of nutritious and delightful food. It also contains a wealth of nutritious ingredients such as proteins, iron, calcium, and vitamins, as well as fibers, and mineral salts that our bodies require. Knowing these plants also aids in preserving the traditional users' cultural heritage. It is crucial to describe, characterize, and search the nutritional content of these plants. This is a very intriguing topic to explore and advance due to the variety of plants that have been harvested and their usage in various industries (nutritional, medical, cosmetic, pharmaceutical). The leaves and stems of some plants are used in cooking or eaten raw in salads. The limited research that has been done in Algeria has not demonstrated the potential contribution of these items to ration balance. The aim of this work was to gauge the chemical composition of two raw and cooked plants, leaves and petioles of wild spinach (Chenopodium vulvaria) and leaves of purslane (Portulaca oleracea), and to apply chemometric methods to obtain information that permit their characterization. Five samples of ten bunches of each plant were purchased from different retail traders in different markets of Constantine (Algeria) and prepared according to standard AFNOR. The samples were analyzed by determining moisture, total lipids, proteins, ash, minerals, dietary fibers, as well as, total carbohydrates and energy value by calculation. A multivariate data analysis method such as principal components analysis (PCA), was applied on a data matrix consisting of the analytical parameters of raw and cooked spinach leaves and petioles; and of raw and cooked purslane leaves. The results obtained showed that the samples of cooked spinach were characterized by the total carbohydrate and nutritional fiber (ADF, NDF and lignin). As for raw spinach, it was associated with lipid, protein, dry matter, energy value, carbohydrate and ash. The PCA of minerals revealed that the leaves are separated from petioles and are richer in minerals. PCA also enabled demonstration of the distinction between raw and cooked purslane. The dry matter content of the leaves of spinach and purslane decreased after cooking. It would be interesting to continue the work by studying the composition of fatty acids, tocopherols and pigments. In addition, the study could be broadened to include other cooking methods, and harvested plants that grow in Algeria. Key words: Spinach (Chenopodium vulvaria), Purslane (Portulaca oleracea), Nutritional composition, Principal Components Analysis, Cooking

Development of thermoplastic starch/poly(butylene adipate‐co‐terephthalate) biobased active packaging films and potential usage in the food industry

AbstractThis study focused on the development and optimization of thermoplastic starch (TPS) and poly(butylene adipate‐co‐terephthalate) (PBAT) biobased active packaging films by incorporation with grape seed oil, alpha‐tocopherol, and rice husk. In the compound and film production step during extrusion for biobased active packaging film two variables, the antioxidant substance concentration (Grape seed oil, 0.5–4%; alpha‐tocopherol, 0.5–4%; rice husk, 1–5%), TPS concentration (60–90%) were optimized through response surface methodology (RSM) for highest tensile strength (MPa) and antioxidant activity (%) as responses. Among the resulting films, samples containing grape seed oil in particular exhibited an optimal balance, showcasing robust mechanical properties alongside potent antioxidant characteristics. Comprehensive analyses encompassing thickness, density, color, opacity, FTIR spectroscopy, total phenolic content, and energy consumption were conducted to assess the films' viability for potential deployment within the food industry. Based on the findings, it was determined that these biobased active packaging films made of TPS/PBAT held promise in safeguarding food products.

Advancing urban water-energy demand predictions with a rotor hopfield neural network model optimized by contracted thermal exchange optimizer

ABSTRACT The relationship between urban water and energy demand is crucial for resource efficiency, sustainability, and environmental conservation. Rapid urbanization, population growth, and climate change necessitate integrated models that capture the complex interdependencies, feedback loops, and trade-offs between water and energy systems. This research addresses this intricate relationship by developing a modified Rotor Hopfield Neural Network (RHNN) Model using input indicators like population data, GDP, water consumption, precipitation, electricity consumption, wastewater discharge, and industrial coal usage. The model is optimized using a modified metaheuristic, called Contracted Thermal Exchange Optimizer (CTEO), resulting in a comprehensive forecast of urban water-energy demand. The model’s superior efficiency is demonstrated by comparing it with other contemporary methods. Upon comparison with alternative approaches, it is clear that the RHNN/CTEO model surpasses them, showcasing a mean relative error of 1.47% for water usage and 2.60% for energy consumption. This leads to an overall average MRE of 2.03%. This research contributes to the existing body of knowledge by offering an advanced model for urban water-energy demand forecasting, providing valuable insights for policymakers, urban planners, and stakeholders in making informed decisions related to resource allocation, infrastructure development, and sustainable urban development.

The environmental impacts and sustainable pathways of the global diamond industry

Mining diamond poses significant and potentially underestimated risks to the environment worldwide. Here, we propose a Diamond Environmental Impacts Estimation (DEIE) model to forecast the environmental indicators, including greenhouse gas (GHG) emissions, mineral waste, and water usage of the diamond industry from 2030 to 2100 in the top diamond production countries under different Shared Socio-economic Pathways (SSPs). The DEIE projection results indicate that the annual GHG emissions, mineral waste, and water usage of the global diamond industry will reach 9.65 Mt, 422.80 Mt, and 78.68 million m3 under the SSP1-1.9 scenario, and 13.26 Mt, 582.84 Mt, and 107.95 million m3 under the SSP2-2.6 scenario in 2100, respectively. We analyze the environmental impact heterogeneities and the associated driving factors across the major diamond production countries identified by our DEIE framework. In addition, we find that lab-grown diamonds can reduce annual GHG emissions, mineral waste, and water usage by 9.58 Mt, 421.06 Mt, and 66.70 million m3 in 2100. The lab-grown diamond substitution policy can annually save 714 million cubic meters of landfill space, harvest 255 million kilograms of rice, feed 436 million people, and lift 1.19 million households out of hunger. The lab-grown diamond substitution policy could contribute to the diamond industry’s GHG mitigation and sustainability efforts in a cost-saving manner.

Rational Bi[sbnd]Mo[sbnd]O nanospheres decorated g-C3N4 for photocatalytic performance of dye degradation

Developing a superior heterostructured composite, optimizing performance through synergies from complementary advantages in diverse materials, remains an ongoing challenge. The rise in organic contaminants in freshwater bodies possesses a major threat to human health. Various dyes such as Rhodamine B, Eosin Y and Methylene Blue are found in excess concentration due to industrial usage and their removal is important for environmental remediation and contaminant-free water. In this study, we report synthesis and fabrication of Bi2MoO6/g-C3N4 (BM/CN) composite photocatalyst and Rhodamine B photodegradation studies. The composite photocatalysts have been characterized using XRD, TGA, FT-IR, UV–Vis, TRPL, SEM, TEM, XPS, electrochemical, and BET studies over g-C3N4, Bi2MoO6, and Bi2MoO6 (10–40 wt%) supported on g-C3N4 photocatalysts. The photocatalytic studies are performed using various composite photocatalysts of BM/CN show 30%-Bi2MoO6 supported on g-C3N4 (30BM/CN) exhibits maximum visible light activation for dye degradation with 3.6 and 2.4-fold higher rate of degradation than bare g-C3N4 and Bi2MoO6, respectively. Higher surface area, broad light absorption, enhanced lifetime, lower charge transfer and higher transient photocurrents of the composite photocatalysts compared to g-C3N4 and Bi2MoO6. Moreover, study is aided with scavenger tests to support the proposed mechanism.The increased photoactivity of 30BM/CN is due to the synergistic interplay of improved charge generation and a reduction in the electron-hole (e−/h+) recombination rate.

Issues of Water Resources in Saudi Arabia: Past, Present, and Future

The present paper addresses a comprehensive historical assessment of water consumption, demand, and supply in Saudi Arabia, along with future projections regarding water balance, in terms of demand and supply by source in various sectors. Being an arid region, Saudi Arabia experiences scorching heat, low precipitation, a high rate of potential evaporation, and the absence of permanent water bodies over the territory. Groundwater contributes almost 61% of total available water, while the recharge rate is negligible. However, few widyan (ephemeral streams) systems exists to satisfy water demand, which could contribute to approximately one year of domestic water consumption if managed efficiently. The study also predicts water consumption scenarios for the next three consecutive development plans, i.e., the 10th plan (2015–2019), 11th plan (2020–2024), and 12th plan (2025–2029). The analysis shows that water consumption may decline significantly in the future, if the present rate of decline continues. Scenario I, if the current rate is assumed, provides a decrease in consumption of 14.36, 12.66, and 11.15 BCM for 10th, 11th, and 12th plans, respectively. Moreover, the domestic and industrial sectors will consume more water in the future. In the same way, scenarios II and III represent a decline in total water consumption, along with that of agriculture, while domestic and industrial water usage would increase, thus improving environmental sustainability.

Impact of industrial robot on labour productivity: Empirical study based on industry panel data

This empirical analysis explores the impact of industrial robots on labour productivity using panel data of 17 Chinese industries from 2006 to 2021. The results reveal that the development of industrial robots significantly improves labour productivity; a series of robustness tests validate this outcome. However, the impact of industrial robots on labour productivity varies across industry types. The influence coefficient of the low-density robotics industry is larger than that of the high-density robotics industry. Furthermore, although the scale of industrial robot usage before 2012 was smaller than that after 2012, its effect on labour productivity was more significant. Our findings indicate the possibility of diminishing marginal effect of industrial robots in promoting labour productivity. The mechanism analysis demonstrates that human capital level has a complete intermediating effect between industrial robots and labour productivity. Thus, industrial robot applications can contribute to labour productivity by optimising human capital structure. These findings provide crucial insights for governments and policy makers to improve labour productivity and economic growth.

Evaluation of long-term properties and life cycle assessment of alkali-activated concrete with varying fiber inclusions

The potential for using alkali-activated concrete (AAC) as a sustainable substitute for Portland cement (PC) concrete is a topic of ongoing research interest. The uncertainties in the raw material composition, the reportedly low tensile strength of AAC, and the lack of relevant standard codes of practice further inhibit wider industrial usage of AACs. Fiber inclusions reportedly enhance the tensile properties of traditional PC concrete. However, there is no long-term data available for fiber-reinforced alkali-activated concrete (FRAAC) mixtures. Hence, the present study is aimed at investigating the long-term mechanical properties of ambient-cured FRAAC with GGBS as the sole precursor. Four different types of fibers, viz., steel (SF), polyvinyl alcohol (PVAF), polypropylene (PPF) and glass (GF) are used in this research as reinforcements in AAC. Tests for compressive strength (fc′), splitting tensile strength (fsp) and flexural strength (fr) are conducted for FRAAC containing varying fiber contents of 0.1–0.3%. The findings demonstrated that fiber additions do not have any noticeable influence on the compressive strength of AAC. However, relative to the plain mix, the fsp and fr of FRAACs were enhanced by up to 32% and 29%, respectively. The formation of the hardened calcium aluminosilicate matrix and evolution of the fiber-matrix bonding over time was evident from the scanning electron microscopy (SEM) images. The related molecular bonds over a period of 7-d to 360-d was identified through mineralogical and chemical analyses. Furthermore, to validate its environmental sustainability, life cycle assessments were conducted. The AACs exhibited at least 43% lesser impacts than PC concrete on the ecosystem. FRAACs had only slightly higher (≤ 7% increase across all fiber types) impacts than plain AAC.