46 research outputs found
Recommended from our members
Fat, oil and grease reduction in commercial kitchen ductwork: a novel biological approach
Recent research has characterised emissions upon cooking a variety of foods in a commercial catering environment in terms of volume, particle size and composition. However, there has been limited focus on the deposition of solid grease in commercial kitchen ductwork, the sustainability of these systems and their implications on the heat recovery potential of kitchen ventilation extract air.
This paper reviews the literature concerning grease, commonly referred to as Fat, Oils and Grease (FOG) abatement strategies and finds that many of these systems fall short of claimed performances. Furthermore these technologies often add to the energy cost of the operation and reduce the potential application of heat recovery in the ventilation ductwork. The aim of this study was to develop and evaluate a novel FOG removal system, with a focus on low environmental impact.
The novel FOG removal system, utilises the biological activity of Bacillus subtilis and associated enzymes. The biological reagent is delivered via a misting system. The temperature, relative humidity and FOG deposit thickness were measured in the ductwork throughout a 3 month trial period. FOG deposit thickness was reduced by 47% within 7 weeks. The system was found to be effective at reducing the FOG deposit thickness with minimal energy cost and impact upon the kitchen and external environment. Internal ductwork operating temperature was measured with respect to future heat recovery potential and a reduction of 7 °C was observed
Recommended from our members
Social practices required for the recovery of cassava waste for heat generation in Thailand
Thailand is a dominant supplier of cassava to world markets, supplying around 67% of the global market, resulting in abundant cassava waste. However, farmers typically discard this waste in cassava fields, and thus it is underutilised as an energy resource. In addition, Thailand’s domestic cassava-based bioethanol plants depend on imported coal to produce heat. To address this challenge, this research investigated the potential for the collection and recovery of cassava waste from farms. Semi-structured interviews were conducted with Thai cassava farmers. Social practice theory was applied to evaluate the effects of cassava waste collection on Thai cassava farmers’ current agricultural practices. Three Thai cassava agricultural activities— land preparation, fertilization application and waste management—would be impacted by this new strategy. The connections between each of these potentially affected activities has been discussed and, ultimately, cassava waste collection by Thai cassava growers was proposed as a new practice. This study concluded that the enhanced use of cassava waste for the production of heat and power could potentially help Thailand meet its renewable energy targets in future
Recommended from our members
An environmental impact assessment of the management of cassava waste: a case study in Thailand
In Thailand, cassava waste is one of the main biomass residues and has the potential to be used as a biomass fuel. However, currently most cassava waste in Thailand is left in agricultural fields or burnt on site and is not utilised for any energy-related purposes.
This research investigates the environmental impacts associated with three cassava waste management practices including (i)- ploughing the waste to the soil (ii)-burning the waste in the field (iii)- collecting and using the waste in cassava-based bioethanol plant. The environmental impact assessment and material flow analysis associated with these management practices were conducted using the Global Emissions Model for Integrated Systems (GEMIS) package [1]. The outcomes of this study reveal that the CO2 emissions associated with these waste management practices are about 0.195, 0.243 and 0.361 kg CO2-eq/kg of as received (wet) cassava waste, respectively. Compared to other cassava waste disposal methods such as ploughing and burning, cassava waste collection would result in the biggest environmental impact, emitting nearly 85% more GHGs than ploughing and 48% more than burning
Recommended from our members
An investigation of the impact of time of generation on carbon savings from PV systems in Great Britain
PV only generates electricity during daylight hours and primarily generates over summer. In the UK, the carbon intensity of grid electricity is higher during the daytime and over winter. This work investigates whether the grid electricity displaced by PV is high or low carbon compared to the annual mean carbon intensity using carbon factors at higher temporal resolutions (half-hourly and daily).
UK policy for carbon reporting requires savings to be calculated using the annual mean carbon intensity of grid electricity. This work offers an insight into whether this technique is appropriate.
Using half hourly data on the generating plant supplying the grid from November 2008 to May 2010, carbon factors for grid electricity at half-hourly and daily resolution have been derived using technology specific generation emission factors.
Applying these factors to generation data from PV systems installed on schools, it is possible to assess the variation in the carbon savings from displacing grid electricity with PV generation using carbon factors with different time resolutions.
The data has been analyzed for a period of 363 to 370 days and so cannot account for inter-year variations in the relationship between PV generation and carbon intensity of the electricity grid. This analysis suggests that PV displaces more carbon intensive electricity using half-hourly carbon factors than using daily factors but less compared with annual ones.
A similar methodology could provide useful insights on other variable renewable and demand-side technologies and in other countries where PV performance and grid behavior are different
Recommended from our members
Evaluation of life cycle cost for the comparison of decentralized waste to composting and landfilling of municipal solid waste
Background
Municipal solid waste (MSW) has increased dramatically in emerging economies like Bangladesh as a result of rapid urbanization and economic growth. Due to the high land requirements and nature of the waste, options of municipal waste management such as landfilling and waste-to-energy have proven to be expensive and inefficient. Previously, a pilot study on a waste-to-compost program in a decentralized facility was done in Dhaka to evaluate the effectiveness of municipal waste management.
Objective
The aim of this study was to analyze the life cycle costs (LCCs) of a waste-to-composting facility in Dhaka, Bangladesh. The objective was to ensure economical and effective management of MSW by comparing overall spending to the current and proposed waste management process.
Methodology
In order to evaluate the potential of the planned decentralized compost plant, LCC methods using UNEP/SETAC guidelines are used in the study. This includes an additional analysis of environmental and operational costs and benefits.
Result
The research found that the overall cost of the decentralized compost facility was $857,110, much less than the expenditures associated with landfilling and conventional composting methods in Dhaka.
Conclusion
This study shows that a decentralized waste-to-compost plant may be a profitable option for dealing with municipal solid waste. Its potential to ease stress on municipal governments is highlighted by its much lower price tag. Insightful for policymakers and urban planners in emerging nations confronting comparable waste management difficulties, this research stresses the need to implement such creative, cost-effective approaches in quickly rising metropolitan cities
Recommended from our members
Assessing the economic and energy efficiency for multi-energy virtual power plants in regulated markets: a case study in Egypt
This paper investigates the design and operation management of VPPs in regulated markets. A new framework based on profit maximization objective function is presented in this study. The hypotheses of this research is that considering profit as an objective function would yield a more realistic and optimal sizes compared to Cost of Energy (COE) minimization approach adopted in literature. The analyzed VPP aggregates solar PV units, CCHP supplying power and thermal energy, Battery storage system and thermal energy storage system. The system is formulated in an optimization model fed by energy demand profile, prices and inputs for solar power (irradiance and weather data). The objective function is formulated based on maximization of profit of the VPP selling power to the grid by Power Purchase Agreement (PPA), selling power to consumers at the public electricity tariff, and selling thermal energy at an assumed constant tariff. CCHP non-linear part-load efficiency is also considered in the model, accordingly, Genetic Algorithm (GA) is employed to solve the optimization. Results of the optimally configured model achieved 36% improvement in COE compared to literature. Solar power contributed by 31% from the total produced energy without imbalance, grid power contributed by 4%, and CO2 emissions reduced by 47% compared to full dependency on the grid. Statistical relationships were drawn showing the relationship between profit, energy and exergy efficiencies versus different CCHP capacities. In addition, analysis is provided for the efficiencies’ relation with the dumped heat from the CCHP
Recommended from our members
Simultaneous sizing and energy management of multi-energy virtual power plants operating in regulated energy markets
This research analyses the case of a Virtual Power Plant (VPP) in regulated electricity markets, trading energy with the consumers and the grid under a Power Purchase Agreement (PPA). The VPP propagates the deployment of solar PVs while balancing its intermittency with a dispatchable power plant, which is assumed in this research to be a CCHP, supplying cooling, heating, and power. The VPP also integrates energy storage systems for a comprehensive assessment. Traditionally, the VPP concept has not been introduced in regulated markets, but it is widely researched in deregulated markets where VPPs trade energy with the electricity grid for profit maximisation. In regulated markets, a special architecture is proposed for a VPP that mediates between residential compounds and electricity grids for profit maximization and energy demand coverage, thus converting the compound into a power generator with minimum dependence on the grid for its energy demand. In the literature on aggregated energy systems in regulated markets, it is usually overlooked to perform detailed energy modelling and optimisation on an hourly level. Only basic rule-based frameworks for energy management are proposed. In this research, it is initially assumed that since the VPP integrates multi-energy components supplying heating, cooling and electricity, optimization of the output of each component for a common profit maximization, is necessary. However, in VPP-related literature, the capacity of each component, which is a main input for energy modelling, is traditionally assumed and not assessed. Therefore, the research aims to explore how to find the optimal capacity configuration of the residential VPP that achieves optimal profit. The paper analyses an iterative exhaustive search framework, integrating the 2-levels of energy optimisation (hourly profit maximisation objective) and capacities optimisation (Life cycle CAPEX & OPEX minimisation). Compared to baseline cases, where only energy optimisation is performed, and capacities are assumed and not assessed in terms of capital investment, the proposed framework achieved a higher annual profit by 3.1% and a payback period of 11 years. The results also provide comprehensive 3D charts drawing the relations between the achieved profit against capacities configurations, thus allowing high-level decision-making. The results also prove the hypothesis that hourly energy optimisation should not be performed without investment cost assessment and that targeting the minimization of investment costs will indirectly benefit the achieved profit
Recommended from our members
Assessing and comparing a DDPG model and GA optimization for a heat and power virtual power plant operating in a power purchase agreement scheme
This paper proposes a deep deterministic policy gradient (DDPG) model for the operation management of a solar power-based virtual power plant (VPP) having a PPA with the grid and supplying power and thermal energy to consumers. The VPP serves to balance the solar power intermittency, cover the demand whenever solar power is absent, and ensure an efficient supply of energy. The literature in this field has introduced optimization algorithms to determine the power plant’s output power or heat on a rolling-horizon basis. Using the function approximation category, which involves reinforcement learning with neural networks, to solve the simultaneous thermal and power operation management of VPPs is still not well developed. The challenges imposed in this model are sourced from the non-linearity of the CCHP, the power and thermal balance constraints, and the consideration of continuous variables rather than discrete ones. A case study is simulated in Egypt to assess and compare the models. Compared to the genetic algorithm optimization, the proposed DDPG model achieved 3% more profit, 12% higher carbon dioxide (CO2) emissions, and 9% lower natural gas consumption. The DDPG solution was 57% faster than the GA. The results of the DDPG model proved that machine learning methods could outperform optimization in terms of optimality achievement and speed of solution. The DDPG improved the operation of energy storage units and was able to recognize the supply-demand operational pattern, ensuring the scalability of the VPP to cope with different energy demand
levels
Recommended from our members
Unsteady flow simulation of a vertical axis augmented wind turbine: a two-dimensional study
As the integration of vertical axis wind turbines in the built environment is a promising alternative to horizontal axis wind turbines, a 2D computational investigation of an augmented wind turbine is proposed and analysed. In the initial CFD analysis, three parameters are carefully investigated: mesh resolution; turbulence model; and time step size. It appears that the mesh resolution and the turbulence model affect result accuracy; while the time step size examined, for the unsteady nature of the flow, has small impact on the numerical results. In the CFD validation of the open rotor with secondary data, the numerical results are in good agreement in terms of shape. It is, however, observed a discrepancy factor of 2 between numerical and experimental data. Successively, the introduction of an omnidirectional stator around the wind turbine increases the power and torque coefficients by around 30–35% when compared to the open case; but attention needs to be given to the orientation of the stator blades for optimum performance. It is found that the power and torque coefficients of the augmented wind turbine are independent of the incident wind speed considered