Future Engineering Research

Specification-driven feasibility and techno-economic assessment of recycling and valorization of HTV silicone rubber manufacturing scrap from composite insulator production

2026-08-01T16:39:35+00:00

Composite silicone insulators are rapidly displacing ceramic and glass units in high-voltage transmission, and their manufacture generates substantial cured high-temperature-vulcanized (HTV) silicone rubber scrap that is chemically inert and non-degradable. This study presents a specification-driven feasibility and techno-economic assessment of recycling and valorizing 24 t/year (2 t/month) of cured HTV scrap (grade MPC D010707) generated in the production of 170 A/B, 170 C and 420 A/B composite insulators. Using the manufacturer quality specification together with the stoichiometry of aluminum trihydroxide (ATH) dehydration, a compositional model of approximately 55 wt% ATH, 5 wt% reinforcing silica, and 40 wt% polydimethylsiloxane (PDMS) is inferred without new laboratory characterization. Three valorization routes are modeled: mechanical recycling by ambient and cryogenic grinding with reincorporation into virgin compound; oxidative pyrolysis to recover a silica-rich inorganic residue; and high-temperature mullite synthesis. A linear property-degradation model, anchored to a recent peer-reviewed high-consistency rubber recyclate dataset, shows that mechanical property loss is not the binding constraint. Instead, the particle-size specification of 10 µm median diameter and the tracking and erosion requirement of class 1A4.5 restrict reincorporation into insulator housings to roughly 10 to 15 parts per hundred rubber (phr) until qualified by testing, whereas downcycled products tolerate up to 60 phr. Stoichiometric mass balances give a 73% inorganic yield for oxidative pyrolysis, of which 375 kg/ton is a high-value silica fraction, and 500 kg/ton mullite for the ceramic route. Techno-economic modeling identifies ambient mechanical recycling as the most attractive first investment, with a capital expenditure near USD 21,000 and a payback below one year, contingent on internal absorption capacity, with recovered-silica pyrolysis as a strategic second phase. The work is presented explicitly as a modeling and techno-economic feasibility study based on manufacturer specification data and literature-derived property models.

Numerical simulation and control measures for deflagration in ventilation ducts of polyimide foam production line

2026-09-16T18:52:20+00:00

Polyimide foam exhibits excellent high-temperature resistance, flame retardancy, and sound absorption performance, and is widely used in aerospace, advanced equipment, and other high-tech fields. However, its production process involves inherent deflagration risks from combustible dust and volatile organic gases. Using the ventilation and dust-collection duct of a polyimide foam production line as the research object, this paper establishes a numerical deflagration model in ANSYS Fluent to investigate the propagation behavior of gas-dust mixtures inside the duct. The evolution characteristics of velocity, temperature, pressure, species distribution, and particle motion are systematically analyzed. Quantitative results show that the peak gas temperature in the main reaction zone reaches approximately 1680 K, the maximum local flow velocity is 24.3 m/s, and the minimum gas density drops to 0.20–0.40 kg/m³. The middle horizontal section of the duct is identified as the primary deflagration zone, while elbows and areas with structural discontinuities are high-risk locations with significant pressure concentration and flow turbulence. Based on this, targeted risk control measures are proposed, including duct structure optimization, explosion venting and isolation arrangement, dust concentration management, online monitoring system construction, and ignition source control. This study provides theoretical support for explosion-proof design and safety optimization of ventilation and dust removal systems in polyimide foam production lines.

Taguchi optimization of hybrid agro-aquatic waste-reinforced epoxy composite for enhanced mechanical performance

2026-09-14T19:30:42+00:00

The choice of engineering materials substantially contributes to the longevity and output–to–input ratio of machines and structures. There is growing interest in sourcing local engineering materials that are more durable than synthetic materials. The mechanical characteristics of these engineering materials must meet acceptable standards. This study examined reinforcing hybrid agro-aquatic waste with epoxy resin as an additive. Since natural materials are biodegradable and renewable, they are an environmentally friendly alternative to conventional materials in engineering applications. This has led to a growing trend in using bio-composites made from natural materials. This study aimed to optimize the strength of hybrid agro-aquatic samples made from bamboo fiber, whelks, clams, periwinkle shells, and palm kernels bonded with resin to improve engineering performance. The composites were made using a hand lay-up technique with varying percentages of shells and fiber (10-30%), mixed with different amounts of epoxy resin. Tensile, impact, flexural, and water absorption tests were performed on the cured samples after 24 hours. The Taguchi L25 (5x5) orthogonal array was used to optimize the experimental trials in Minitab 18, with five design parameters at five levels each. The results showed an average flexural strength of 114.87 MPa, while the unreinforced bio-composite had 72.33 MPa. Turning agricultural waste into engineering materials such as bamboo fiber, clams, palm kernel shells, whelk shells, and periwinkle shells can positively impact the building industry.

A survey on intelligent transportation systems for accident detection and risk prediction using AI

2026-09-27T02:32:37+00:00

The complexity of today's transportation networks has made road safety, accident detection, and risk management increasingly challenging. Intelligent Transportation Systems (ITS) offer a technological base to enhance transportation safety by incorporating sensors, communications, data processing, and intelligent decision-making. This review discusses the application of Artificial Intelligence (AI) for accident detection and risk prediction in ITS by examining information sources, methodologies, accident investigation, and associated challenges. It examines the use of machine learning, deep learning, computer vision, spatiotemporal modeling, and multimodal AI for accident detection, risk prediction, severity analysis, and active safety management. This review of studies suggests that collecting traffic, vehicle, road, environmental, geographical, historical, and driver-related information can improve understanding of collisions and support timely safety decisions. The review also highlights the importance of explainable and reliable AI that provides transparency, dependability, and accountability. However, the obstacles include data heterogeneity, model generalization, dynamic conditions, scalability, quick implementation, privacy considerations, security requirements, and compatible information systems. In conclusion, incorporating AI in ITS may have the power to shift road-safety management from reactive measures to data-driven approaches.