Effect of Roasting Temperature on the Quality and Acceptability of Dakuwa

The effect of roasting temperature on the quality of dakuwa was studied with a view to ascertaining the best temperature at which to roast the maize grains and groundnut for the production of dakuwa. Maize grains and groundnut were germinated for 72 hours after which they were dried and roasted at 120, 130, 140 and 1500C. The groundnut was decoated after which both maize and groundnut were milled separately. After milling, the maize flour and groundnut paste were mixed together in equal ratio. To this mixture, 10% and 5% respectively of table sugar and granulated red pepper were added. The mixture was then milled and moulded into balls. The dakuwa produced were analysed for proximate composition, mineral content, microbial count and organoleptic properties using standard methods. Results of proximate composition and mineral content showed significant (p<0.05) variations in moisture (3.2-5.9%), protein (16.5-19.1%) and iron (0.00-0.03mg/100g) contents. The total microbial count, colour and overall acceptability also differed significantly (p<0.05). The sample roasted at 1400C had the best results. Keywords: Dakuwa, roasting, proximate composition, mineral, sensory

IoT Based Real Time Energy Management of Virtual Power Plant using PLC for Transactive Energy Framework

The high penetration of renewable sources owing to less environmental pollution creates challenges for the grid operators. Virtual Power Plant is a novel concept that will integrate the small distributed energy resources and will act as a single conventional power plant in the electricity market. As a core energy management system in VPP, the energy should be dispatched optimally for achieving the maximum profit. Therefore, smart energy management is developed in this article of VPP with PLC and IoT in a unified market environment that integrates the DA and RT market. The cost characteristics for the interruptible load, battery storage system are modelled individually. The proposed scheme can efficiently handle the energy demand for the VPP domain. Four different scenarios are considered with different loading conditions for validation of the concept of smart energy management. The profitability for each scenario is shown with the experimental results

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

Renewable energy sources prevail as a clean energy source and their penetration in the power sector is increasing day by day due to the growing concern for climate action. However, the intermittent nature of the renewable energy based-power generation questions the grid security, especially when the utilized source is solar radiation or wind flow. The intermittency of the renewable generation can be met by the integration of distributed energy resources. The virtual power plant (VPP) is a new concept which aggregates the capacities of various distributed energy resources, handles controllable and uncontrollable loads, integrates storage devices and empowers participation as an individual power plant in the electricity market. The VPP as an energy management system (EMS) should optimally dispatch the power to its consumers. This research work is proposed to analyze the optimal scheduling of generation in VPP for the day-ahead market framework using the beetle antenna search (BAS) algorithm under various scenarios. A case study is considered for this analysis in which the constituting energy resources include a photovoltaic solar panel (PV), micro-turbine (MT), wind turbine (WT), fuel cell (FC), battery energy storage system (BESS) and controllable loads. The real-time hourly load curves are considered in this work. Three different scenarios are considered for the optimal dispatch of generation in the VPP to analyze the performance of the proposed technique. The uncertainties of the solar irradiation and the wind speed are modeled using the beta distribution method and Weibull distribution method, respectively. The performance of the proposed method is compared with other evolutionary algorithms such as particle swarm optimization (PSO) and the genetic algorithm (GA). Among these above-mentioned algorithms, the proposed BAS algorithm shows the best scheduling with the minimum operating cost of generation

Physicochemical and Sensory Properties, and In-Vitro Digestibility of Biscuits Made from Blends of Tigernut (Cyperus esculentus) and Pigeon Pea (Cajanus cajan)

Objective: The study explored the potential of tigernut and pigeon pea flour blends in the preparation of biscuits. Materials and methods: Tigernut and Pigeon pea seeds were processed into flour and formulated into blends. The chemical composition of the flours and biscuits prepared from the flour blends as well as in-vitro (starch and protein) digestibility, physical and sensory properties of the biscuits were evaluated using standard methods. Results: The chemical composition such as protein, moisture, fat, ash, crude fiber, ash, energy, iron, calcium, zinc and phosphorus contents of composite biscuits ranged from (11.64 to 17.81%), (4.11 to 6.03%), (12.80 to 18.40%), (2.43 to 3.63%), (3.81 to 4.95%), (437.84to 453.36kcal) , (3.18 to 3.81mg/100g), (60.15 to 87.69mg/100g), (0.50 to 1.27mg/100g) and (223.19 to 248.17mg/100g) respectively and were significantly (p. 0.05) higher than 100 % wheat biscuit. The composite biscuits had poor starch digestibility (25.43 to 44.18 %) than 100 % wheat flour biscuit (57.25 %) as well as high protein digestibility (60.20 to 71.57 %). Biscuits prepared from tigernut and pigeon pea flour blends were significantly (p. 0.05) higher in width and spread ratio than control samples while their break strength decreased with increase in pigeon pea flour addition. There were no significant (p.0.05) differences in appearance, flavour, crust colour and overall acceptability between composite biscuits and control. Conclusion: This study reveals that tigernut and pigeon pea flour could be used in the production of nutritious biscuit and confirms their potential as a functional food especially for diabetic and obese patients due to their low starch and high protein digestibility

Assessment of the Antimicrobial Activities of Ximenia caffra (Sour Plum) Leaf

The antimicrobial activities of sour plum (Ximenia caffra) leaf extracts was investigated against Staphylococcus Aureus, Bacillus Subtilis, Escherichia Coli and Pseudomonas Aurignosa, using Methanol, n-Hexane and Chloroform as solvents for extraction. The agar wall diffusion method as described by Irobi et al., (1994) was used for the determination of antimicrobial activity. The results showed that the extracts were all bacterial static with the methanol and chloroform extract, having the highest and least activities, respectively. Ximenia caffra leaves are therefore recommended for use as antimicrobial agents

Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework

Renewable energy sources prevail as a clean energy source and their penetration in the power sector is increasing day by day due to the growing concern for climate action. However, the intermittent nature of the renewable energy based-power generation questions the grid security, especially when the utilized source is solar radiation or wind flow. The intermittency of the renewable generation can be met by the integration of distributed energy resources. The virtual power plant (VPP) is a new concept which aggregates the capacities of various distributed energy resources, handles controllable and uncontrollable loads, integrates storage devices and empowers participation as an individual power plant in the electricity market. The VPP as an energy management system (EMS) should optimally dispatch the power to its consumers. This research work is proposed to analyze the optimal scheduling of generation in VPP for the day-ahead market framework using the beetle antenna search (BAS) algorithm under various scenarios. A case study is considered for this analysis in which the constituting energy resources include a photovoltaic solar panel (PV), micro-turbine (MT), wind turbine (WT), fuel cell (FC), battery energy storage system (BESS) and controllable loads. The real-time hourly load curves are considered in this work. Three different scenarios are considered for the optimal dispatch of generation in the VPP to analyze the performance of the proposed technique. The uncertainties of the solar irradiation and the wind speed are modeled using the beta distribution method and Weibull distribution method, respectively. The performance of the proposed method is compared with other evolutionary algorithms such as particle swarm optimization (PSO) and the genetic algorithm (GA). Among these above-mentioned algorithms, the proposed BAS algorithm shows the best scheduling with the minimum operating cost of generation