Muhammad Rizwan Azhar

Conventional steam- and hydro-distillation of essential oils from woody biomass are constrained by diffusion-limited mass transfer, incomplete recovery of structurally entrapped oil, and long processing times. This study demonstrates a vacuum-assisted thermo-mechanical process intensification strategy for extraction of Santalum album essential oil using instantaneous controlled pressure drop (CID) technology. The cyclic process combines saturated steam treatment (~6 bar) with rapid decompression to deep vacuum (3–30 mbar), inducing tissue expansion, oil-cell rupture, and permeability-controlled oil release.Pilot-scale experiments using 5 kg batches identified an optimum operating window of 80–140 pressure-drop cycles, corresponding to a steam severity index of 500–1200 bar·s. Compared with conventional steam and hydro-distillation, CID increased essential oil recovery by up to 50% while reducing extraction time from 24 h to less than 1 h without compromising oil quality. Microscopic analysis confirmed extensive oil-cell rupture and resin canal exposure, providing mechanistic evidence for the transition from diffusion-controlled distillation to permeability-controlled extraction. Scale-up identified condensation capacity as the principal engineering constraint, while spray-jacket cooling and volume-compensation limited thermal heterogeneity to approximately 5%. These findings establish transferable operating windows and scale-up principles for vacuum-assisted thermo-mechanical extraction, providing a process engineering framework for intensifying diffusion-limited solid–fluid extraction systems.

Conventional steam- and hydro-distillation of essential oils from woody biomass are constrained by diffusion-limited mass transfer, incomplete recovery of structurally entrapped oil, and long processing times. This study demonstrates a vacuum-assisted thermo-mechanical process intensification strategy for extraction of Santalum album essential oil using instantaneous controlled pressure drop (CID) technology. The cyclic process combines saturated steam treatment (~6 bar) with rapid decompression to deep vacuum (3–30 mbar), inducing tissue expansion, oil-cell rupture, and permeability-controlled oil release.Pilot-scale experiments using 5 kg batches identified an optimum operating window of 80–140 pressure-drop cycles, corresponding to a steam severity index of 500–1200 bar·s. Compared with conventional steam and hydro-distillation, CID increased essential oil recovery by up to 50% while reducing extraction time from 24 h to less than 1 h without compromising oil quality. Microscopic analysis confirmed extensive oil-cell rupture and resin canal exposure, providing mechanistic evidence for the transition from diffusion-controlled distillation to permeability-controlled extraction. Scale-up identified condensation capacity as the principal engineering constraint, while spray-jacket cooling and volume-compensation limited thermal heterogeneity to approximately 5%. These findings establish transferable operating windows and scale-up principles for vacuum-assisted thermo-mechanical extraction, providing a process engineering framework for intensifying diffusion-limited solid–fluid extraction systems.

The selective recovery of lithium from spent lithium iron phosphate (LFP) batteries remains challenging due to their low economic value and complex chemistry. This study develops a low temperature oxalic acid leaching process without external oxidants for the semi selective recovery of lithium from industrial LFP black mass. Process optimisation was conducted using Box-Behnken response surface methodology (RSM) integrated with kinetic modelling. The effect of temperature (35-70 oC), oxalic acid concentration (0.3-1.0 M), and solid to liquid (S/L) ratio (20-100 g/L) were evaluated, with reaction progress monitored over 180 min. The S/L ratio was the dominant factor influencing lithium extraction, while temperature and acid concentration showed comparatively weaker effects within the studied ranges. Equilibrium was typically achieved within 60-90 min.,Kinetic analysis indicates that the Avrami model describes the leaching behaviour, suggesting a mixed diffusion and reaction-controlled mechanism. Lithium is released via proton driven lattice dissolution, while iron precipitates as FeC₂O₄·2H₂O, forming a product layer that limits mass transfer. This is supported by a low activation energy (12.86 kJ/mol) and Avrami exponent (n = 0.47 ± 0.35). The process demonstrated high lithium-iron selectivity (Li/Fe = 6.63), with moderate separation from aluminium and phosphorus.,This study provides a fundamental kinetic and process optimisation framework for lithium recovery. Optimised conditions (35 oC, 0.71 M oxalic acid, 45.4 g/L S/L) yielded 96% lithium recovery, closely matching model predictions. The findings demonstrate that efficient lithium recovery is achievable at ambient temperature without agents, providing a practical framework for sustainable battery recycling process design.

This study presents a full-scale, industry-deployed artificial intelligence (AI) framework integrating machine learning (ML), simulation, and operational boundary definition across a nitrogen-removal WWTP in Australia. Up to nine years of high-frequency online instrumentation data (≤419,000 records per target; 5-30 min resolution) and ~3,000 laboratory samples were used to train and evaluate 19 ML algorithms across 13 hydraulic, energy, pressure, and effluent-quality targets across full-scale WWTP.Extra Trees (ExT) and deep neural networks (NN) demonstrated superior performance. Deterministic operational variables, including plant inflow, aeration flow, blower energy, effluent pump power and speed, and ocean discharge pressure, were predicted with R2 ~ 0.96-0.99 on validation and unseen datasets. Biological and laboratory-derived parameters achieved R² ~ 0.86-0.92, reflecting intrinsic process variability. NN models enabled extrapolative simulation beyond historical operating ranges.The models generated and evaluated millions of feature combinations to define safe operational boundary conditions associated with screen bypass, overpressure risk, energy overuse, and effluent non-compliance. The framework enabled dynamic optimisation of aeration and pumping configurations, improved energy efficiency, and provided robust ML-based soft sensors during instrumentation faults. This work establishes a scalable pathway for embedding AI into real-world WWTP engineering infrastructure, measurable improvements in predictive accuracy, operational resilience, and risk-aware optimisation.

Monitoring water quality in urban environments is increasingly vital due to growing pressures from urbanisation, climate change, and stormwater runoff. Multispectral satellite remote sensing offers a promising, scalable approach for assessing water quality across large spatial and temporal scales compared to conventional in-field sampling and laboratory analysis. This research assesses the capability of Sentinel-2 satellite multispectral imagery to track the water quality index (WQI) of the Swan-Canning River system and nearby coastal areas around the central business district (CBD) of Western Australia (WA) from 2018 to 2025. Satellite-derived optically active parameters such as chlorophyll-a, turbidity, and absorption of coloured dissolved organic matter were used to compute the trophic state index, the turbidity index, and the absorption by coloured dissolved organic matter index, subsequently generating a WQI raster output using a fuzzy inference system (FIS). Spatiotemporal analysis revealed that poor water quality (WQI 40-60) dominated the study area, particularly in stagnant urban lakes, while localised zones of good quality persisted near estuarine and coastal regions. Seasonal trends showed that rainfall and temperature fluctuations were key drivers of short-term deterioration, while dissolved oxygen concentrations were inversely correlated with poor WQI scores. Despite limitations in data availability due to cloud cover, the study demonstrates that satellite-based multispectral imaging combined with FIS modelling offers a robust, scalable framework for urban freshwater quality monitoring. The findings emphasise the urgent need for targeted catchment management and real-time adaptive strategies in rapidly urbanising regions.