Bioplastics are gaining attention as eco-friendly alternatives to conventional plastics, with Polybutylene Adipate Terephthalate (PBAT) emerging as a promising biodegradable substitute for polyethylene (PE) in food packaging. Commercial PBAT is often blended with other plastics or bio-based fillers to improve mechanical properties and reduce costs, though these additives can influence its environmental footprint. Therefore, this study quantifies the environmental impacts of producing PBAT resin blends reinforced with common inorganic fillers and compares end-of-life (EoL) performance against PE. While prior studies have largely assessed virgin PBAT or PBAT/Polylactic Acid (PLA) systems, systematic LCA of commercial-style PBAT blends with inorganic fillers and screening LCA level for comparisons of composting vs. landfill remain limited. The contributions of this study are to: (i) map gate-to-gate environmental hotspots for PBAT-blend conversion, (ii) provide a screening gate-to-grave comparison of PBAT composting vs. PE landfill using ReCiPe 2016 and IPCC GWP100 methods, and (iii) discuss theoretical implications for material substitution in the context of EoL strategies. The results indicated that producing 1 kg of PBAT blend generated a single score impact of 921 mPt with Human Health and Resource categories contributing similarly, and a GWP of 8.64 kg CO₂-eq, dominated by mixing and drying processes. EoL screening showed PBAT composting offered clear advantages over landfilling PE, yielding −53.9 mPt and 11.35 kg CO₂-eq savings, effectively offsetting production emissions. In contrast, landfilling PE resulted in 288.8 mPt and 2.2 kg CO₂-eq emissions. Sensitivity analysis further demonstrated that a 30% reduction in electricity use could decrease impacts by up to 10%, underscoring the importance of energy efficiency improvements and renewable energy adoption for sustainable PBAT development.

Biopolymer blends have attracted considerable attention in industrial applications due to their notable mechanical properties and biodegradability. This work delves into the innovative combination of butadiene-acrylonitrile (referred to as NBR) with a pectin-based biopolymer (NGP) at a 90:10 mass ratio through a detailed analysis employing mechanical characterization, Fourier transform infrared (FTIR) analysis, thermogravimetric analysis (TGA), and morphology studies using SEM. Additionally, biopolymer's biodegradability under aerobic and anaerobic conditions is tested. The study's findings underscore the superior tensile strength and elongation at break of the NGP/NBR blend in comparison to pure NBR, while also exhibiting a decrease in puncture resistance due to imperfect bonds at the particle-matrix interfaces, necessitating the use of a compatibilizer. In anaerobic conditions, evaluation of biodegradable properties reveals 2% and 12% biodegradability in NBR and NGP/NBR blend, respectively. The degradation properties were also aligned with TGA results highlighting a lower decomposition temperature for NGP. Additionally, this research integrates the application of a conditional value-at-risk (CVaR)-based analysis of the blend's tensile properties to evaluate the uncertainty impact in the experiment. Under risk, a significant enhancement in the tensile performance (by 80%) of the NGP/NBR blend was shown compared to pure NBR. Ultimately, the study shows that adding pectin to the NBR compound amplifies the overall performance of the biopolymer significantly under select criteria.

Natural fiber-based biocomposites are rapidly gaining traction in sustainable manufacturing. However, their supply chain (SC) designs at the feedstock pre-processing stage often lack robust multicriteria decision-making evaluations, which can impact downstream processes and final product quality. This case study proposes a sustainability-driven multicriteria decision-making framework for selecting pre-processing equipment configurations within a hemp-based biocomposite SC. Using a cradle-to-gate system boundary, four alternative configurations combining balers (square vs. round) and hammer mills (full-screen vs. half-screen) are evaluated. The analytical network process (ANP) model is used to evaluate alternative SC configurations while capturing the interdependencies among environmental, economic, social, and technical sustainability criteria. These criteria are further refined with the inclusion of sub-criteria, resulting in a list of 11 key performance indicators (KPIs). To evaluate ranking robustness, a non-linear programming (NLP)-based sensitivity model is developed, which minimizes the weight perturbations required to trigger rank reversals, using an IPOPT solver. The results indicated that the Half-Round setup provides the most balanced sustainability performance, while Full-Square performs best in economic and environmental terms but ranks lower socially and technically. Also, the ranking was most sensitive to the weight of the system reliability and product quality criteria, with up to a 100% shift being required to change the top choice under the ANP model, indicating strong robustness. Overall, the proposed framework enables decision-makers to incorporate uncertainty, interdependencies, and sustainability-related KPIs into the early-stage SC design of bio-based composite materials.

Bioplastics are gaining attention as eco-friendly alternatives to conventional plastics, with Polybutylene Adipate Terephthalate (PBAT) emerging as a promising biodegradable substitute for polyethylene (PE) in food packaging. Commercial PBAT is often blended with other plastics or bio-based fillers to improve mechanical properties and reduce costs, though these additives can influence its environmental footprint. This study assessed the environmental impacts of producing and disposing of fossil-based PBAT blends containing bio-based fillers through a gate-to-gate life cycle assessment (LCA) and a screening-level gate-to-grave end-of-life analysis using the ReCiPe 2016 method and IPCC’s 100-year Global Warming Potential (GWP) in SimaPro. Producing 1 kg of PBAT blend generated 921 mPt in total environmental impact, with Human Health and Resource categories contributing similarly, and a GWP of 8.66 kg CO₂-eq, mainly from mixing and drying. End-of-life analysis revealed that composting PBAT offered clear advantages over landfilling PE, yielding –53.9 mPt impact and 10.7 kg CO₂-eq savings, ef-fectively offsetting production emissions. In contrast, landfilling PE caused 288.8 mPt impact and 2.2 kg CO₂-eq emissions. Sensitivity analysis showed that reducing electricity use by 30% could lower impacts by up to 10%, underscoring the importance of energy efficiency and renewable energy adoption for sustainable PBAT development.

Natural fiber-based biocomposite are increasingly favored in green engineering for their sustainable properties. Designing their Supply Chains (SCs) requires strategic decisions around pre-processing equipment while accounting for environmental, economic, social, and technical criteria. This article aims to introduce a comprehensive sustainability-based multicriteria decision-making framework for a hemp-based biocomposite SC design, using an Analytical Network Process (ANP) model incorporating both inner and outer dependencies among decision criteria and alternatives. Also, a nonlinear optimization-based sensitivity analysis model is proposed to assess the robustness of the decisions. A combination of full/half screen hammer mill and square/round baler, as pre-processing equipment options, are evaluated along with input data uncertainty. Evaluating four preprocessing scenarios, results show the round baler with half-screen hammer mill as the most sustainable option, while the full-screen and square baler setup performs best economically and environmentally. The trade-off analysis highlighted key drivers: demand-sensitive pricing, carcinogenic impact, job creation, and product quality.

This study investigates the application of Bayesian Belief Networks (BBNs) to optimize thermoforming, an energy-intensive manufacturing process that rapidly shapes thermoplastic composite sheets into precise three-dimensional forms. Thermoforming involves complex interactions among process parameters, presenting ongoing challenges in achieving optimal energy efficiency, temperature stability, and consistent product quality. Further, the process can become highly non-linear with multiple sources of noise, due to the nature of the factory and logistical factors, adding further challenges to effectively predicting the process outcomes. To address this challenge, in this case study a Bayesian Belief Network was developed to capture the inherent uncertainties and intricate relationships among critical process variables. This probabilistic model integrated experimental observations, computational modeling results, and expert-derived insights, enabling a robust and adaptive decision-making support for the process designers. Each of the select key process parameters such as convective Heat Transfer Coefficient (HTC), and heating power, were prioritized based on their influence on the process performance indicators such as the end product quality, energy usage, temperature distribution error, settling time, and stability. Results demonstrated that the BBN framework provides an effective interactive decision support tool capable of continuous model refinements through updating of probabilities as new data became available, while achieving enhanced energy efficiency and process control, thereby also reducing the operational cost and material wastage.