Comparison of the photoactivity of TiO2 coatings using a flat panel reactor and FTIR to monitor the CO2 evolution rate

A system designed to continuously monitor the gas phase for the UV irradiation of flat panels of organic coatings has been modified to compare the photocatalytic degradation of organic pollutants using TiO2 functional coatings. TiO2 was formulated into pastes and was coated onto various stainless steel and glass fibre meshes. The photoactivity was determined by monitoring the photodegradation of acetone and following the rate of CO2 evolution using FTIR spectroscopy. The kinetics were compared to the photoinduced degradation of indigo carmine, followed by UV-Vis spectroscopy to determine whether the CO2 evolution method is a viable, rapid alternative to photodegradation monitoring. A correlation was established between the two methods by determining the rate constants of the decolourisation of indigo carmine and CO2 evolution, demonstrating that such a method can be used as a rapid assessment of the photoactivity of photocatalytic coatings

Thermal sintering of printable copper for enhanced conductivity of FTO coated glass substrates

Copper inks potentially provide a cost effective replacement to silver for printed electronic circuits. In glass based applications such as PV or smart glass, it can provide a means of conductivity enhancement or additional functionality. Three inks consisting of a mixture of nano and micro copper particles were systematically studied to examine the relationship between sintering temperature, sintering time and gaseous environment on the electrical qualities of the sintered printed films deposited on FTO coated glass. There is a definite interaction between the particulate nature of the ink, the sintering conditions and the conductive properties of the film. Films containing only nano-particles provide the most conductive films with optimum sintering conditions of temperatures of 225 °C for 60 min. The inclusion of micro particles increased the ideal sintering temperature but lowered the sintering time. An ink containing an equal mixture of nano and micro particles exhibited the lowest performance and this could be attributed to partial oxidation of the nano-particles along the conductive path, which occurs as a result of the presence of the micro particles

Role of Smart-Release Pigments in Preventing Corrosion Driven Cathodic Disbondment of Organically Coated Hot Dip Galvanised Steel

The role of smart-release corrosion inhibitive pigments in preventing cathodic delamination of organically coated hot-dip galvanized steel (HDG) is investigated. The pigments consisted of hydrotalcite (HT) exchanged with a range of inorganic and organic anionic species and were dispersed in a model PVB coating. A scanning Kelvin probe (SKP) technique was used to determine cathodic delamination rates, and the inhibition efficiencies obtained for inorganic ions increased in the order ${{{\rm{CO}}}_{3}}^{2-}$ < ${{{\rm{MoO}}}_{4}}^{2-}$ < ${{{\rm{NO}}}_{3}}^{-}$ < ${{{\rm{VO}}}_{4}}^{3-}$ < ${{{\rm{WO}}}_{4}}^{2-}$ < ${{{\rm{PO}}}_{4}}^{3-}$ < ${{{\rm{CrO}}}_{4}}^{2-}.$ The inhibition efficiencies for organic-based pigments increased in the order triazole <phenylphosphonate <trans-cinnamate <benzoate <salicylate <benzotriazole. The inhibition efficiency afforded by the best performing organic inhibitor, benzotriazole (BTA), rivalled that of HT containing stored chromate anions. Findings are consistent with HT-BTA acting to sequester anions from the underfilm electrolyte, releasing BTA− which subsequently strongly adsorbs on the underfilm metal surface but can also form an insoluble Zn-BTA precipitate at the coating-defect boundary

A Multi-Criteria decision making (MCDM) methodology for high temperature thermochemical storage material selection using graph theory and matrix approach

Industrial waste heat is currently underutilized due to the techno-economic challenges, inherent variability and intermittency of this source. To overcome the existing barriers, reduce the emission of greenhouse gases and protect the global environmental conditions, energy recovery is one of the most effective strategies. In the design of heat storage systems, the material selection procedure plays an important role and requires complex interrelationships between the various factors and parameters to be elucidated toachieve the best candidate material for a given application. This paper presents a Multi-Criteria Decision Making (MCDM) methodology based on Graph Theory and Matrix approach for high temperature thermochemical storage (TCS) material selection. Furthermore, the presented approach has been used to select the suitable candidate material for recovering the high temperature waste heat (over 500 °C) in Port Talbot Steelworks

Enhancing Self-consumption of PV-battery Systems Using a Predictive Rule-based Energy Management

A predictive real-time Energy Management System (EMS) is proposed which improves PV self-consumption and operating costs using a novel rule-based battery scheduling algorithm. The proposed EMS uses the day-ahead demand and PV generation forecasting to determine the best battery scheduling for the next day. The proposed method optimizes the use of the battery storage and extends battery lifetime by only storing the required energy by considering the forecasted day-ahead energy at peak time. The proposed EMS has been implemented in MATLAB software and using Active Office Building on the Swansea University campus as a case study. Results are compared favorably with published state-of-the-arts algorithms to demonstrate its effectiveness. Results show a saving of 20% and 41% in total energy cost over six months compared to a forecast-based EMS and to a conventional EMS, respectively. Furthermore, a reduction of 54% in the net energy exchanged with the utility by avoiding the unnecessary charge/discharge cycles

Fuzzy Predictor With Additive Learning for Very Short-Term PV Power Generation

Photovoltaic (PV) power generation is highly intermittent in nature and any accurate very short-term prediction can decrease the impact of its uncertainties and operation costs and boost the reliable and efficient integration of PV systems into micro/smart grids. This work develops a new generalized technique for very short-term prediction of PV power generation from the lagged power generation data using fuzzy techniques. A preprocessor extracts relevant statistical features from the PV data which are fed to the fuzzy predictor. A modified version of Wang-Mendel training algorithm is employed to directly extract the fuzzy rules from the training data pairs. This methodology exploits the limited training data more efficiently. In addition, an online additive learning routine is proposed, which enables the predictor to learn from new data while running the predictions. So, the prediction accuracy increases over time and the predictor updates to account for long-term changing conditions of weather and PV system performance and its surroundings. Numerical results of the comparison of the proposed approach with simple fuzzy and traditional artificial neural network methods on a live PV system in the United Kingdom demonstrate its improved prediction accuracy, outperforming the benchmark approaches with a normalized mean absolute error (NMAE) of 3.6%

Perovskite solar cells in N-I-P structure with four slot-die-coated layers

The fabrication of perovskite solar cells in an N-I-P structure with compact titanium dioxide blocking, mesoporous titanium dioxide scaffold, single-step perovskite and hole-transport layers deposited using the slot-die coating technique is reported. Devices on fluorine-doped tin oxide-coated glass substrates with evaporated gold top contacts and four slot-die-coated layers are demonstrated, and best cells reach stabilized power conversion efficiencies of 7%. This work demonstrates the suitability of slot-die coating for the production of layers within this perovskite solar cell stack and the potential to transfer to large area and roll-to-roll manufacturing processes

Development and characterisation of an alginate and expanded graphite based composite for thermochemical heat storage

Thermochemical heat storage is one of the most attractive technologies to store heat from solar thermal energy or waste heat from industrial processes for its high energy density and long-term storage capability. This research presents a novel expanded graphite/alginate polymer matrix encapsulated with hydrated salts as highly efficient thermochemical heat storage materials. Through the simple synthesis method, the composite material can be sized and shaped to fit multiple applications, and be easily scaled where needed. Through the reversible hydration and dehydration reaction, the incorporated CaCl2 salt can store and release heat. Thermal energy from solar thermal generators or low grade waste heat sources (< 200 °C) is appropriate for the dehydration of CaCl2. A salt loading value of 84% has been achieved with visible porosity maintained. Static heat is used to study the charge reaction, whereas a flow of humid air through a packed bed is used to study the discharge reaction where temperature uplifts between 10–14 °C were observed. A vermiculite/CaCl2 composite is used as a comparison in both reactions. Additionally, bulk density, surface porosity, surface area, moisture sorption and thermal conductivity are considered. The results show that the novel composite materials developed in this study can achieve better packing density and comparable energy density comparing to the conventional vermiculite/CaCl2 composite, but with higher thermal conductivity leading to enhanced energy efficiency

MILP Optimized Management of Domestic PV-Battery Using Two Days-Ahead Forecasts

This paper proposes an Energy Management System (EMS) for domestic PV-battery applications with the aim of reducing the absolute net energy exchange with the utility grid by utilizing the two days-ahead energy forecasts in the optimization process. A Mixed-Integer Linear Programming (MILP) exploits two days-ahead energy demand and PV generation forecasts to schedule the day-ahead battery energy exchange with both the utility grid and the PV generator. The proposed scheme is tested using the real data of the Active Office Building (AOB) located in Swansea University, UK. Performance comparisons with state-of-the-art and the commercial EMS currently running at the AOB reveal that the proposed EMS increases the self-consumption of PV energy and at the same time reduces the total energy cost. The absolute net energy exchange with the grid and the total operating costs are reduced by 121% and 54% compared to the state-of-the-art and 194% and 8% when compared to the commercial EMS over a six-month period. Furthermore, the results show that the pro-posed method can reduce the energy bill by up to 46%for the same period compared to the state-of-the-art. The paper also investigates the effect of using different objective functions on the performance of the EMS and shows that the proposed EMS operate more efficiently when it is compared with another cost function that directly promotes reducing the absolute net energy exchange