Lagouge Kwanda Tartibu

Coal-fired power stations have generated substantial quantities of legacy dumped coal ash, creating long-term environmental and land-use challenges. This study evaluates naturally weathered legacy dumped coal ash as an improvement material for low-traffic road sublayer rehabilitation without cement, lime, or other chemical stabilisers. Three mixtures containing 0%, 30%, and 40% ash by mass were assessed using particle size distribution, Atterberg limits, modified compaction analysis, soaked California Bearing Ratio (CBR), swell testing, X-ray fluorescence (XRF) analysis, and QEMSCAN automated mineralogical analysis. The untreated gravel exhibited a Plasticity Index (PI) of seven and a soaked CBR of 30%. At 30% ash, the PI decreased to four and the soaked CBR increased modestly to 33%, while the 40% mixture became non-plastic, exhibited the lowest swell, and recorded a CBR of 29%. The results indicate that the 30% ash mixture provided the highest bearing capacity among the investigated mixtures, whereas the 40% ash mixture provided superior moisture stability. QEMSCAN automated mineralogical analysis showed that the ash-containing mixtures were dominated by aluminosilicate-, quartz-, and glass-associated mineral phases, providing additional mineralogical evidence of modification of the gravel matrix following ash incorporation. The 30% and 40% mixtures displaced equivalent proportions of conventional gravel while beneficially reusing an existing industrial residue. The findings demonstrate measurable material-sustainability benefits through reduced conventional aggregate demand and beneficial legacy-ash utilisation while maintaining acceptable engineering functionality, supporting its potential as a secondary construction material for resource-efficient and sustainable low-volume road rehabilitation.

In this work, we propose a novel passive circuit theory on fractional High-Pass (HP) Negative Group Delay (NGD) topology by using the fractional order reactive impedance and admittance. The fractional order reactive networks are derived from the generalized exponential kernel fractional derivative function. We analytically show that the proposed fractional order topology is capable to satisfy the existence conditions of the HP-NGD function. The design equations of HP-NGD function were derived with respect to the targeted specifications. The established synthesis methods are compared with those of the state-of-the-art on the HP-NGD circuit specifications. The proof of concept (POC) of generalized exponential Kernel fractional HP-NGD circuit is identified from Oustaloup approximation-based impedance and admittance. Study cases of POCs confirm the feasibility of the established HP-NGD circuit theory by means of simulations with a commercial tool. The calculated model and simulation outcomes confirm the HP-NGD behavior and specifications as expected from the proposed theoretical design. The obtained results present NGD minimal values and HP-NGD cut-off frequency of about (−32.9 ns, 45.3 kHz) and (−650 ns, 2.2 kHz) with low-attenuation less than 1 dB from the POC circuits of the fractional-capacitor and fractional- inductor based NGD circuits, respectively. To the best of our knowledge, this is the first time that the fractional order capacitor and inductor whose impedance and admittance were derived based on the generalized exponential kernel fractional derivative is applied to the NGD circuit.

Growing cooling demand and environmental concerns surrounding mechanical vapour compression systems motivate research into alternative technologies capable of converting low-grade heat into useful cooling. Silica-gel/water single-stage dual-bed adsorption chillers (ADCs) are promising candidates, however, their design must balance conflicting performance targets. This study proposes a regression-assisted multi-objective optimisation framework for low-grade-heat ADC, combining statistically validated surrogate models with the Ant Lion Optimiser and its multi-objective variant. Three co-equal objectives, coefficient of performance (COP), cooling capacity (Q_cc) and waste-heat recovery efficiency (η_e) are jointly maximised to map an operational envelope for sustainable cooling. Two-dimensional Pareto-optimal solutions exhibit a one-dimensional ridge in which η_e declines, and COP and Q_cc increase simultaneously. Within the explored bounds, non-dominated ranges span COP=0.675–0.717, Q_cc=18.3–27.5 kW and η_e=0.118–0.127, with a practical compromise near COP ≈ 0.695, Q_cc ≈ 24 kW and η_(e )≈ 0.122–0.123. While mass flow rate decisions increase Q_cc at the expense of η_e, a one-at-a-time sensitivity analysis with re-optimisation identifies the hot- and chilled-water inlet temperatures and exchanger conductance as the dominant decision variables and maps diminishing-return regions. The proposed framework can effectively use low-grade heat in future low-carbon buildings and processes and supports the configuration of ADC systems.

Miniaturization remains a critical challenge for Negative Group Delay (NGD) circuit design. This paper presents an inductorless bandpass (BP) NGD integrated circuit (IC) designed based on an RC-topology leveraging 180-nm complementary metal-oxide-semiconductor (CMOS) technology. The schematic and layout of the BP-NGD CMOS IC prototype are designed with CADENCE VIRTUOSO® commercial tool. The compact BP-NGD chip area is 1.06 mm × 0.55 mm. The key performance metrics include a center frequency of 18.67 MHz, an NGD of -0.91 ns, a bandwidth of 74.31 MHz, a maximum insertion loss of -3.86 dB, and a minimum return loss of -11.04 dB. The results demonstrate that the IC design effectively balances miniaturization with high-performance NGD characteristics, providing a promising solution for NGD integration in system-on-chip (SoC) applications.

Energy efficiency is widely recognized as a critical strategy for reducing energy consumption in industrial systems. Improving energy efficiency has become a central point in industrial systems aiming to reduce energy consumption and operational costs. Industrial air compressors are among the most energy-intensive assets and often operate under static control policies that fail to adapt to real-time dynamics. This paper proposes a cognitive digital twin (CDT) framework that integrates reinforcement learning as, especially, a Proximal Policy Optimization (PPO) agent into the virtual replica of the air compressor system. CDT learns continuous from multidimensional telemetry which includes power, outlet pressure, air flow, and intake temperature, enabling autonomous decision-making, fault adaptation, and dynamic energy optimization. Simulation results demonstrate that PPO strategy reduces average SEC by 12.4%, yielding annual energy savings of approximately 70,800 kWh and a projected payback period of one year. These findings highlight the CDT potential to transform industrial asset management by bridging intelligent control.