Decarbonising greenhouse food production requires improvements in thermal management, energy efficiency, and system integration. Greenhouse energy demand is shaped by coupled heat and mass transfer processes, particularly envelope performance, ventilation, and latent heat associated with humidity control. This article synthesises recent advances in greenhouse microclimate control with emphasis on heat transfer, low-carbon heating and cooling, thermal storage, renewable and waste heat integration, and advanced modelling and control approaches. The review shows that humidity control and latent load management are primary drivers of winter energy use, as moisture removal through ventilation and dehumidification directly increases the sensible heating required to maintain indoor temperature setpoints. When assessed using realistic psychrometric relationships, ventilation and dehumidification can dominate peak heating demand and seasonal consumption. The performance of heat pumps, storage systems, semi-closed greenhouse concepts, and renewable heat pathways depends on how thermal loads are defined, how system boundaries are set, and how technologies are integrated in operation. Digital twins, predictive control, and hybrid physics-data models are increasingly used to manage variability in weather, energy prices, and infrastructure constraints. Greenhouse decarbonisation cannot be treated as a simple substitution of energy sources. System performance depends on coordinated design and operation, including heat recovery, moisture removal, and integration of supply technologies. Semi-closed and heat recovery-based configurations can reduce the ventilation–heating penalty and lower primary energy demand compared with vent-to-dry approaches. Long-term market projections suggest that the commercial greenhouse sector could expand substantially by 2050 under plausible growth scenarios, reflecting increased capital investment rather than a proportional rise in global food output. Net-zero greenhouse production is achievable through combined improvements in thermal management, electrification, and renewable energy integration. However, large-scale deployment depends on consistent modelling assumptions, credible economic assessment, and alignment with heat and CO2 supply infrastructure. The transition is therefore shaped by system integration and planning as much as by individual technologies.

The paper presents a methodology to improve the operating robustness of low-cost magnetic drive-train (MDT) systems in which load-side sensing is not a preferred option for addressing pole-slipping and variable torsional stiffness issues. Firstly, through dynamically analysing the relative displacement angle between both sides of the MDT (resulting from the developed electromagnetic- and load-torque), the paper offers an operating criteria using the inertia ratio, electromagnetic- and load-torque excitations to prevent the MDT from pole-slipping. Subsequently, the relationship between controller parameters and dominant/resonant poles of closed-loop MDT control system, is discussed. It is shown that controller parameters for MDTs to accommodate a wide range of torsional stiffness variations can be determined from natural frequencies that are bounded by operating constraints. Using the presented principles, desired performance with respect to speed reference tracking and load-torque disturbance accommodation can be achieved by simply determining the natural frequency of the dominant pole. Simulation studies and experimental measurements on a custom MDT test facility are used to underpin the efficacy of the proposed analysis and design techniques.

In this study, an assessment of the performance of an industrial gas turbine during compressor fouling conditions is undertaken. The assessment is carried out through performance diagnostic and prognostic methods. The diagnostic technique identifies and isolates faults in the components of the gas turbine, and considers the health parameter of the compressor (efficiency). The remaining useful life of the compressor is estimated by a degradation model that also considers the compressor efficiency health index. Compressor efficiency is estimated using a gas turbine real-time executable digital-twin. The digital-twin is integrated within a gas turbine system operating in the field. Data processing of the compressor efficiency is required to obtain a meaningful trend of the compressor health index. Results demonstrate that the diagnostic and prognostic methods applied to the problem of compressor health parameter estimated by the digital-twin can be a powerful tool to initiate maintenance actions on associated axial compressors prior to significant performance deterioration.

In this study, a sensor fault diagnostic system to detect/isolate and accommodate faults in sensors from an industrial gas turbine has been developed. The sensor fault diagnostic module is integrated with a gas turbine realtime executable digital-twin (RT xDT) reported in a previous study. The sensor fault diagnostic module of the digital-twin considers analytical sensor redundancy using a reference engine model to provide redundant estimates of measured engine variables. A Software-in-the-Loop (S-i-L) architecture and Hardware-in-the-Loop (H-i-L) facility are constructed to assess the sensor diagnostic module (fault detection/ fault isolation) during failure in sensors from the engine. The results demonstrated that if the discrepancy between virtual measurement ( provided by digital-twin) and sensor measurement exceeds the prescribed tolerance levels, the sensor fault diagnostic logic determines the state of switching between the virtual and engine sensor measurements in a dual lane control configuration of the gas turbine control system. The sensor fault detection system implemented in the gas turbine RT xDT can be deployed onto a distributed control system of industrial gas turbines to diagnose sensor deficiencies and ensure continuous and safe operation of the gas turbine. Consequently, the developed system will increase engine availability and reliability by diagnosing engine operational deficiencies before severe failure.

The building and transportation sectors are responsible for the greatest proportion of energy consumption in cities. While they are intrinsically interlinked with urban built form and density, climate change and technological innovation are having an effect on their relative contributions. This paper aims to develop an optimisation framework to facilitate the identification of the most energy-efficient urban built forms and urban geometry for the future built environment that can be adapted to the changing climate and ongoing technological development. It examines future scenarios for the city of London as a temperate climate zone (as a case study), in 2050, and contrasts it with the present situation. Specifically, the impact of climate change along with the penetration of electric vehicles into the transportation system that can be charged via rooftop photovoltaics is investigated. This study initially develops the geometrical models of four selected urban built forms and, secondly, analyzes their energy performance using an urban energy simulation software. The results, showing the impact of future scenarios on building energy performance, urban built form and density, demonstrate that court and tunnel-court built forms show better energy performance for future development. It is therefore recommended that for future urban developments in London, deep plan court and tunnel-court buildings with a lower number of storeys and a large cut-off angle are more advantageous in terms of building energy to accommodate the expected climate change. Finally, results of simulation trials indicate that the total building energy demand in 2050 is considerably higher than in the present climate as a result of additional cooling load and electric vehicle charging load.

The paper presents an analysis of magnetic drive train (MDT) pole-slipping and an amelioration strategy to reduce uncontrolled load-side speed when subject to pole-slipping.Initially, the pole-slipping inducements of MDTs are deduced based on the assumption that driving controllers are designed with sufficient servo rigidity.Subsequently, in order to avoid initiating degrading coupling torque oscillations, the transient response of the induced coupling torque is analysed.Next, the paper provides a load-side speed reduction methodology based on load-side speed estimation, synchronization, and piecewise electromagnetic torque excitation to reduce the potential for (mechanical or temperature induced) damage to mechanical components (eg.bearings) as a result of uncontrolled speed during pole-slipping.More generally, consideration of electromagnetic torque production for flexible systems, such as MDTs, possessing variable/nonlinear stiffness, is addressed to ameliorate the chances of inducing pole-slipping.

This article presents a high performance and low-cost design methodology for the servo control of magnetic drive-trains (MDTs) operating in direct drive mode. For the first time, this article considers using sensitivity peaks to analyze the robustness and stability of MDT control systems. Initially, through analysis of a dynamic model, the key spring characteristic parameters with respect to operating points, are developed. It is also shown that a wider dynamic performance envelope can be achieved by linearizing the MDT model at around 60 $\%$ –80 $\%$ of the maximum coupling torque, as opposed to traditional linearization under zero torque conditions. Subsequently, this article exploits the spring characteristics for a design methodology based on the $\text{H}_\infty$ mixed sensitivity approach to determine suitable control parameters. Following this, the maximum exogenous load-torque disturbance and speed reference that will not induce pole-slipping can be determined. Finally, preferential position reference profiles and optimal gains for position controllers are given to prevent demand-induced speed oscillations. The proposed methodologies are validated through simulation and experimental studies.

The paper introduces new techniques to reduce the potential for pole-slipping induced by control systems and presents a low-cost pole-slipping detection and recovery scheme for magnetic drive-trains (MDTs). For the first time, the paper shows that a combination of electromagnetic and load-torque excitations which individually are not greater than the maximum coupling torque can initiate pole-slipping. For applications where acceleration feedback is unavailable, the motor-side inertia is virtually increased with a tracking differentiator to provide feedback of acceleration. Subsequently, controller design and parameter optimization are discussed. Experimental measurements on a custom test facility verify the presented principles that low-bandwidth controller designs with low inertia ratios can accommodate a wider range of on-load startup torque and load-torque disturbances without pole-slipping. To address overload issues, a pole-slipping detection method based on the kurtosis of electromagnetic torque and a recovery strategy based on converting the state of pole-slipping into that of on-load startup are presented. Experimental results demonstrate that detecting slip anomalies without load-side information, and recovery from pole-slipping without auxiliary mechanical devices are both feasible.

 Abstract — This paper presents the research and application of model predictive scheduled charging of electric vehicles (EV) subject to limited available power resource. To focus on algorithm and operational characteristics, the EV interface to the source is modelled as a battery state equation during the charging operation. The researched methods allow for the priority scheduling of EV charging in a multi-vehicle regime and when subject to limited source power availability. Priority attribution for each connected EV is described. The validity of the developed methodology is shown through the simulation of different scenarios of charging operation of multiple connected EVs including non-scheduled and scheduled operation with various numbers of vehicles. Performance of the developed algorithms is also reported with the recommendation of the choice of suitable parameters.

: The paper proposes an IoT controlled platform to remotely monitor and control appliances in the residential sector. An IP-based synchronized wireless mesh network is implemented through IoT hardware (based on a NodeMCU) and Google Sheets to monitor and schedule the operation of aggregated domestic refrigerators under a Model Predictive Control (MPC) scheme. Benefits afforded by the proposed technique are investigated through experimental trials from VonShef 13/291 (50W), iGENIX IG 3920 (55W) and Russell Hobbs RHCLRF17B (50W) domestic refrigerators sited in three different domestic locations in the city of Lincoln, UK. Results demonstrate the ability to monitor and control widely distributed networks of refrigerators and adaptively schedule the appliances to reduce peak operational loads and facilitate Demand Side Response (DSR). Further widespread expansion of the proposed technique would allow for a rapidly deployed regional DSR strategy to aid grid stability. Ultimately the underlying principles also could be used for the co-ordinated scheduling of other distributed appliances and equipment, both domestic and industrial.

The paper presents an identification method based on adaptive frequency tracking for the realtime identification of load-side inertia of magnetic drive trains encompassing variable torsional stiffness. Firstly, through analyzing the transient response of motor-side speed it is shown that the response incorporates a ramp component and a damped sinusoidal signal with a frequency approximately equals to the resonant frequency. Subsequently, a second order bandpass filter is designed to remove the ramp component and combined with an adaptive notch filter (ANF) forms a fourth order ANF. With the resonant frequency identified by the ANF, the moment of load-side inertia is then estimated. Finally, a numerical implementation method based on automatically calculating the damping ratio along with bandwidth, gain, and sampling frequency, is presented, and an iterative identification procedure is provided to solve problems associated with the limited duration of oscillations existing in the step response. Results show that the proposed methodology identifies the moment of load-side inertia with an accuracy of 3.3%.

A comparison of electrochemical impedance measurements of batteries for electric vehicles is considered in this paper. Harmonics calculated by Fourier transform following signal excitation are used to determine the battery impedance. Broadband excitation signals are shown to much better than single frequency sinusoids for rapid battery impedance measurement and they are also more readily applied. It is shown that the use of pseudo random ternary sequences provides the improved identification performance compared with other commonly employed methods.

In the case of the widespread adoption of electric vehicles (EV), it is well known that their use and charging could affect the network distribution system, with possible repercussions including line overload and transformer saturation. In consequence, during periods of peak energy demand, the number of EVs that can be simultaneously charged, or their individual power consumption, should be controlled, particularly if the production of energy relies solely on renewable sources. This requires the adoption of adaptive and/or intelligent charging strategies. This paper focuses on public charging stations and proposes methods of attribution of charging priority based on the level of charge required and premiums. The proposed solution is based on model predictive control (MPC), which maintains total current/power within limits (which can change with time) and imparts real-time priority charge scheduling of multiple charging bays. The priority is defined in the diagonal entry of the quadratic form matrix of the cost function. In all simulations, the order of EV charging operation matched the attributed priorities for the cases of ten cars within the available power. If two or more EVs possess similar or equal diagonal entry values, then the car with the smallest battery capacitance starts to charge its battery first. The method is also shown to readily allow participation in Demand Side Response (DSR) schemes by reducing the current temporarily during the charging operation.

Battery impedance is one of the most important parameters to reflect performance and health for electric vehicles and other applications, and is affected by many factors, such as temperature, battery type and aging. Based on the characteristics of pseudo random binary signals, this research presents the design, implementation and verification of methods to determine the battery impedance by using PRBSs under various conditions of temperature and age. The impact of injected current amplitude is also investigated. Results from simulation studies and measurements on a custom experimental test facility show the potential for using pseudo random binary sequences for real-time nonlinear battery system identification and show that it can provide an attractive alternative to more traditional online measurement techniques which can incur protracted measurement intervals.

In this study, the inter-stage dynamic performance of a multistage axial compressor is simulated through a semi-empirical model constructed in the Matlab Simulink environment. A semi-empirical 1-D compressor model developed in a previous study has been integrated with a 0-D twin-shaft gas turbine model developed in the Simulink environment. Inter-stage performance data generated through a high-fidelity design tool and based on throughflow analysis are considered for the development of the inter-stage modeling framework. Inter-stage performance data comprise pressure ratio at various speeds with nominal variable stator guide vane (VGV) positions and with hypothetical offsets to them with respect to the gas generator speed (GGS). Compressor discharge pressure, fuel flow demand, GGS and power turbine speed measured during the operation of a twin-shaft industrial gas turbine are considered for the dynamic model validation. The dynamic performance of the axial-compressor, simulated by the developed modeling framework, is represented on the overall compressor map and individual stage characteristic maps. The effect of extracting air through the bleed port in the engine center-casing on transient performance represented on overall compressor map and stage performance maps is also presented. In addition, the dynamic performance of the axial-compressor with an offset in VGV position is represented on the overall compressor map and individual stage characteristic maps. The study couples the fundamental principles of axial compressors and a semi-empirical modeling architecture in a complementary manner. The developed modeling framework can provide a deeper understanding of the factors that affect the dynamic performance of axial compressors.

The paper presents a two-degree-of-freedom control method that exploits the spring dynamics of a magnetic coupling for electromechanical torque transfer. Initially, through analysis of a dynamic model and linearisation, it is shown that control parameters designed by previous reported methods can be too aggressive, and that a broader performance envelope can be obtained by linearising at around 75% of the pull-out torque. Secondly, the paper presents a method of identifying the spring dynamics without measuring the load-side position of the coupling. Finally, input shaping and the identified parameters are employed to provide a new controller design methodology. The internal model principle is used along with the proposed linearisation scheme to construct a feed-forward/feedback control scheme whose parameters are calculated to provide desired tracking and disturbance rejection characteristics. Experimental results show excellent reference tracking and load torque disturbance rejection performance, whilst reducing the possibility of pole-slipping resulting from aggressive control action.

The paper considers the impact of various temperature hysteresis bands on the projected annual energy consumption of a domestic refrigerator when operating empty of product and with an internal product (comprising of 10L of water). Measurements from an IoT system employing a NodeMCU-based Generalized Predictive Control scheme, are used to support the study. A ThingSpeak platform and Smart Wi-Fi plug is used to realize the different temperature hysteresis bands whilst maintaining a given mean nominal internal temperature. Experimental measurements are taken from an IGENIX IG 3920 refrigerator. Results show that by judicious choice of hysteresis band, which is shown to be dependent on the internal product characteristics, annual energy savings of up to 20% can be obtained compared to worst case fixed hysteresis band scenarios.

The paper introduces a novel indicator of urban built form termed Form Signature. Generic models of four urban built forms are developed including pavilion, terrace, court and a newly introduced tunnel-court which are used to compare and contrast their land-use performance and density characteristics. Selecting plot ratio and site coverage as the most popular and appropriate density indicators, the simultaneous relationship to each of the considered urban built forms is shown graphically with the number of storeys, plan depth and cut-off angle as the main variables of interest. For existing urban areas, the resulting graphs provide a robust tool for statistical analysis of contexts such as climate, economy, energy and crime potential and establish their relationship to form and density. To show the value of the contribution, analysing 32 case studies from 19 cities in different global locations showed an insignificant relationship between climate and form/density of urban areas, while practically depicting that urban areas built in court form acquire higher cut-off angle compared to terrace form urban developments. For the planning of future urban areas, the resulting relationships provide an application-oriented urban planning tool to facilitate the most effective land-use method in order to achieve sustainable cities. Examples showing the potential of the tool for future statistical energy and social analysis of urban areas are provided. Finally, a relative comparison shows that the newly-introduced tunnel-court form achieves the greatest density while pavilion achieves the lowest.

The paper proposes a new, robust cluster-based classification technique for Novelty Identification in sensor networks that possess a high degree of correlation among data streams. During normal operation, a uniform cluster across objects (sensors) is generated that indicates the absence of novelties. Conversely, in presence of novelty, the associated sensor is clustered distinctly from the remaining sensors, thereby isolating the data stream which exhibits the novelty. It is shown how small perturbations (stemming from noise, for instance) can affect the performance of traditional clustering methods, and that the proposed variant exhibits a robustness to such influences. Moreover, the proposed method is compared with a recently reported technique, and shown that it performs 365% faster computationally. To provide an application case study, the technique is used to identify emerging fault modes in a sensor network on a sub-15MW industrial gas turbine in presence of other abrupt, but normal changes that visually might otherwise be interpreted as malfunctions.