The co-pyrolysis of hazardous antibiotic sludge (AS) and waste tires is a promising valorization pathway; however, its implementation is hindered by high activation energy barriers, the release of toxic nitrogenous gases (e.g., HCN), and the formation of low-quality bio-oil. To address these challenges, this study synthesized a bimetallic hierarchical zeolite (Ni-Co/AZ) to modulate the co-pyrolysis pathways of AS with polyurethane/ rubber tires. Hydrogen spillover, generated from tire-derived radicals on the Ni-Co alloy surface, suppressed HCN formation and promoted aromatization. Thermokinetic analysis revealed that Ni-Co/AZ shifted the reaction mechanism from random chain scission (F1) to a three-dimensional diffusion-controlled model (D3), reducing the apparent activation energy by more than 50% (to approximately 28 kJ/mol). In-situ gas monitoring and liquid-phase analysis confirmed that this hydrogen spillover redirected toxic HCN precursors into gaseous NH3 and stable nitrogenous compounds (i.e., fatty nitriles and aromatic amines), achieving gas-liquid biphasic detoxification. Concurrently, the catalyst promoted deoxygenation via decarboxylation and hydrodeoxygenation, reducing the liquid-phase oxygenate content to 5.8%. The size-exclusion effect of the hierarchical zeolite network coupled with bimetallic active sites enabled microporous shape-selective aromatization, increasing the yields of monoaromatic hydrocarbons and light fuel gases while suppressing heavy polycyclic tars. Building upon these mechanistic insights, machine-learning-driven multi-objective optimization was employed to predict temperature and feedstock combinations that maximized co-pyrolysis efficiency and volatile product selectivity. This study provides molecular-level insights into metal-acid bifunctional catalysis, establishing an efficient and sustainable route for valorizing multiple solid waste streams.
The co-gasification of waste polypropylene pyrolysis oil (WPPO) and cashew nut shell (CNS) was investigated in a two-stage downdraft gasifier to produce hydrogen-rich, low-tar gas. Experiments were conducted under air (ER = 0.3) and steam (S/C = 2.5) conditions, with selected runs incorporating activated carbon (AC) in the secondary reactor. Steam gasification achieved a maximum hydrogen concentration of 77.9 vol%. The WPPO/CNS mixing ratio affected performance, with the 1:1 blend increasing CGE from 70.3 to 89.7% to 108.9% and CCE from 68.2 to 71.5% to 83.9%, along with enhanced gas yield. The use of AC reduced gas-phase tar, and a downstream dried AC impinger further decreased tar concentration to 0.45 mg/Nm3. In contrast, the composition of condensed tar showed a shift toward heavier species during steam gasification. GC-MS analysis at a WPPO/CNS ratio of 1:1 indicated that steam gasification promoted the formation of heavier PAHs due to enhanced thermal cracking and subsequent polymerization of aromatic intermediates.
Motivated by the demographic effects, we propose and analyze a novel opinion dynamics model with density-dependent interactions and a nonlinear communication function reminiscent of the celebrated Hegselmann–Krause (H–K) model. The model also incorporates heterogeneity through intrinsic opinion tendencies, leading to richer dynamics. In the homogeneous case, where all agents share the same intrinsic tendency, the relative dissimilarities between opinions converge to zero and a single cluster forms. In contrast, under heterogeneous tendencies, we prove that multiple clusters emerge together with velocity alignment within each cluster, provided suitable structural conditions on the system parameters. These theoretical findings are further supported by numerical simulations, which offer qualitative insights beyond the analytical results.
This study examines South Korea's national freight rail network as a complex system, showing that its efficiency–vulnerability trade-off in fact operates along three separable dimensions — efficiency, vulnerability, and investment cost — compounded by spatial governance mismatches. Integrating topology, attack simulation, cascading failure modeling, and community detection with economic data, we find that targeted removal of just 12–15% of high-centrality nodes fragments the network, versus 35–40% for random failures, reflecting a scale-free rich-club architecture. On the exact physical KORAIL topology (N = 53, E = 86), load-proxy centrality measures (Betweenness ρ = 0.448; Degree ρ = 0.534, both p < 0.05) remain significantly coupled with cascading impact, while structural measures (Closeness, Eigenvector) do not — a dichotomy confirmed by permutation testing, robust to tolerance-parameter variation but attenuated under demand-weighting. Economic importance correlates with structural and adaptive resilience (r = 0.737, 0.831, both p < 0.001) but not with functional or recovery resilience (both non-significant), indicating the economic–resilience synergy is topological rather than operational. Hub-hardening alone cannot resolve the trade-off, as budget constraints, peripheral cascade pathways, and cross-jurisdictional governance mismatches (34.6% of functional communities misaligned with administrative boundaries, Q = 0.414) each impose distinct, irreducible constraints. We propose a "Strategic Resilience Engineering" framework combining targeted hub protection, rich-club redundancy, and governance aligned with functional economic corridors.
