Biomass-fuelled combined heat and power: integration in district heating and thermal-energy storage

Conventional approaches towards energy-system modelling and operation are based upon the system design and performance optimization.In system-design optimization,the thermal or mechanical characteristics of the systems providing for the heat or electricity demands were derived separately without integration with the energy source and without interaction with demand,which results in low-efficiency energy performance.This paper presents a key review on the integration of biomass-powered combined heat and power(BCHP)systems in district-heating systems as well as coupling with thermal-energy storage.In BCHP design,the appropriate sizing of the associated components as part of the district-heating system is very important to provide the optimal dispatch strategy as well as minimized cost and environmental impact while it co-operates with thermal-energy storage.Future strategies for the feasibility,evaluation and integration of biomass-powered energy systems in the context of district systems are also studied.

Development of a Soft Actor Critic deep reinforcement learning approach for harnessing energy flexibility in a Large Office building

This research is concerned with the novel application and investigation of‘Soft Actor Critic’based deep reinforcement learning to control the cooling setpoint(and hence cooling loads)of a large commercial building to harness energy flexibility.The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock.Soft Actor Critic is a model-free deep reinforcement learning technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems.Such control techniques are seen as one possible solution to supporting the operation of a smart,sustainable and future electrical grid.This research tests the suitability of the technique through training and deployment of the agent on an EnergyPlus based environment of the office building.The agent was found to learn an optimal control policy that was able to minimise energy costs by 9.7%compared to the default rule-based control scheme and was able to improve or maintain thermal comfort limits over a test period of one week.The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables.The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates.It was found that the agent requires minimal training sample points and outperforms the baseline after three months of operation and also without disruption to thermal comfort during this period.The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.

Biodiesel from fish waste oil: synthesis via supercritical methanol and thermodynamic optimization

This study evaluates the potential of local fish waste oil as a feedstock for biodiesel via supercritical methanol transesterification(SCMT).Hexane was used as a cosolvent and the transesterification reaction was carried out in a continuous reactor under supercritical conditions.The response surface methodology(RSM)method was applied to analyse the effect of four independent variables,including the weight ratio of methanol to fish-waste oil(W),the reaction temperature(T),the pressure(P)and the feed flow rate(F),on the yield of the biodiesel production in supercritical methanol.According to the calculated optimal operating condition for the RSM,the values of W(22.3 weight ratio of methanol to fish waste oil),T(270°C),P(112.7 bar)and F(2.0 mL min-1)were achieved.Under the optimum conditions,the highest yield was estimated to be 94.6%(g/g).The obtained yield was found to be close to the theoretical yield(95.2%).This value suggests that the proposed strategy has a promising potential in the production of biodiesel fuel.