Mehran Ektesabi

Food waste is a heterogeneous, moisture-rich substrate with substantial potential for renewable methane recovery, but variability in composition, hydrolysis and reactor stability constrains reliable anaerobic-digestion performance. This study presents a literature-informed computational scenario-screening framework that translates fungal pretreatment descriptors into model-ready variables for anaerobic digestion. Published experimental and modelling studies were used to parameterise food-waste composition, spent coffee grounds (SCG), enzyme-targeted pretreatment, anaerobic-digestion operating conditions, methane potential and heuristic risk indicators; no new experimental measurements were generated. The Python-based framework links fungal pretreatment severity with substrate solubilisation, hydrolysis, methane yield, raw-biogas CH₄ fraction, gross methane energy recovery and operational-risk indicators. Under the specified model assumptions, heuristic stability-adjusted methane output methane screening output increased from 272.5 L CH₄ kg⁻¹ VS in the untreated control to 291.5 L CH₄ kg⁻¹ VS under high pretreatment and 295.5 L CH₄ kg⁻¹ VS under extended pretreatment, while increasing SCG inclusion and high organic loading reduced the corresponding screening outputs. These directional responses are partly imposed by the model structure and are therefore interpreted as comparative scenario outputs and experimentally testable hypotheses rather than independent quantitative predictions or process optima.

This paper proposes a three-parameter gearshift scheduling strategy that has been implemented on both large and small electric vehicles with two-speed transmission systems. The new strategy evaluates vehicle performance under varying driving conditions on flat and hilly roads by assessing the vehicle speed, acceleration, and road grade. A heuristic approach is used to develop two gearshift schedules for vehicle acceleration and road grade, and gradient descent and pattern search methods are applied to optimize the gear ratios and primary gearshift schedules. The results show that the proposed gearshift strategy saves 16.5% of energy on hilly roads compared to conventional approaches. Optimal gearshift schedules for acceleration provide more room for second gear operation, while optimized gearshift schedules for the road grade increase the buffer zone for larger vehicles and allow more space for the second gear operating area. The experimental results validate the proposed approach’s performance for both large and small electric vehicles.

Maximum power point tracking (MPPT) techniques have been vastly researched and developed in order to obtain the maximum terminal power of photovoltaic (PV) arrays in the solar renewable energy system. The aim of this paper is to present a new principal scheme-based review of the categorised MPPT methods (conventional, novel, and hybrid) with respect to the deployment of their input variables (solar irradiance, PV arrays’ temperature, and PV arrays’ terminal voltage and current), where MPPT methods are categorised to six different schemes. For each scheme, previous MPPT studies are extracted from literature and analysed. Then the critical benefits and limitations of the six presented MPPT schemes are compared and discussed. It is concluded that those MPPT schemes deploying the measured external variables would be able to track the global maximum power point with high reliability; however, their implementation cost and applicability remains as a challenge due to increasing the sensor deployment cost and complexity. The conclusion of this paper will help new researchers to deliberately select an appropriate MPPT scheme based on their projects’ objectives and limitations, prior to selecting an optimisation algorithm for MPPT.

A novel gearshift scheduling strategy has been framed for a two-speed transmission system in electric vehicles that can save energy during hilly driving and frequently changing driving conditions through efficient electric motor operation. Unlike the traditional approach, the proposed gearshift strategy is based on the preferred vehicle speed range, vehicle acceleration, and road grade to ensure desired vehicle performances with minimum energy consumption. Meanwhile, the vehicle speed range is chosen around the electric motor rated speed, and two gearshift schedules in relation to vehicle acceleration and road grade are developed based on the motor torque generating capacity and efficiency. Appropriate gear is selected through a combined assessment of the required vehicle speed, acceleration, and road grade information. A guideline is developed and explained for the primary gearshift schedule. Next, the gear ratios and gearshift schedules are optimized combinedly in a Simulink environment using the gradient descent method and pattern search method on three driving cycles separately. Depending on the driving scenarios, around 4% to 7.5% energy saving has been experienced through optimization, while the gear ratios and gearshift schedule in relation to the road grade are found to be major contributors to the vehicle economic driving compared to that with the gearshift schedule for vehicle acceleration.

Electric vehicles (EVs) with multi-speed transmission offer improved performances compared to those with single speed transmission system in terms of top speed, fast acceleration, or gradeability along with driving range. In this study, relevant literature is extensively analyzed to explor