• Energy Research
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The theoretical study of chrysanthemin (cyanidin 3-glucoside) as a pigment for TiO2-based dye-sensitized solar cells (DSSCs) was performed with the GAUSSSIAN 09 simulation. The electronic spectra of neutral and anionic chrysanthemin molecules were calculated by density functional theory with B3LYP functional and DGDZVP basis set. A better energy level alignment was found for partially deprotonated molecules of chrysanthemin, with the excited photoelectron having enough energy in order to be transferred to the conduction band of TiO2 semiconductor in DSSCs. In addition, we used the raw aqueous extracts of roselle (Hibiscus sabdariffa) calyces as the source of chrysanthemin and the extracts with various pH values were tested in DSSCs. The extracts and photosensitized semiconductor layers were characterized by UV-Vis spectroscopy, and DSSCs based on raw extracts were characterized by current density-voltage measurements.

A theoretical and experimental study of a conventional boost converter is presented. Based on the real behavior of the components, the conventional boost converter model dealing with both inductive and capacitive losses as well as switching losses is introduced. From this model, the detailed analytical expressions of the voltage gain factor and the conversion efficiency are established taking into account the losses due to parasitic resistances and switching losses. The behavior of the converter is then analyzed by simulating the voltage gain factor and the conversion efficiency as a function of the duty cycle. The converter prototype was manufactured and a set of experimental measurements was made; these measurements made it possible to demonstrate that the proposed theoretical models were reliable for a large range of duty cycle for the boost converter.

The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%.

This work investigated the prediction of the optimum height and tilt angle of the solar receiver of a 30 kWe solar tower power plant for the electricity production in the Sahelian zone. Initially, the solar field is sized to determine the total reflecting surface area of the mirrors and the number of heliostats. A PS10-like radially staggered heliostat field is used to design the heliostat layout in the field using a Matlab code. The concentrated solar flux at the input of the receiver was determined using Soltrace software by the Monte Carlo ray tracing (MCRT) method. The sizing results show that the total reflecting surface area is 350 m2 for an optical efficiency of 76.4% and a reference DNI of 600 W/m2. The solar field layout indicates 175 heliostats of 2 m2 surface area and 1.5 m height each. The simulation results show that the optimum height and tilt angle of the solar receiver are 26 m and 65°, respectively.

Climate change and variability are significant challenges for the environment and food security worldwide. Development strategies focusing simultaneously on adaptive farming, productivity, and reducing greenhouse gas (GHG) emissions-known as climate-smart agriculture (CSA) strategies-are key to responding to these challenges. For almost a decade, within the framework of Climate Change, Agriculture and Food Security (CCAFS), World Agroforestry (ICRAF), and its partners have been using Participatory Action Research (PAR) to fully engage key stakeholders in co-creating such CSA development strategies. This includes the testing of Agricultural Research for Development (AR4D) CSA scalability options. The multidisciplinary teams include the National Research and Extension Systems (NARES), national meteorological services (NMS), non-profit organizations (NGOs), and local radio programs, among others. The CCAFS-West Africa Program, World Agroforestry-West and Central Africa (ICRAF-WCA), International Union for Conservation of Nature (IUCN), University of Reading, and Centre Régional de Formation et d'Application en Agro-météorologie et Hydrologie Opérationnelle (AGRHYMET) provide technical backstopping to the national teams. Climate information (CI) was used as an entry point to inform the development of CSA technologies and practices within Climate-Smart Villages (CSV). This groundwork has led to a greater understanding of three critical factors for successful CSV implementation: (1) Building strong partnerships to co-design and develop agricultural systems that improve ecosystem and population resilience, (2) Key stakeholders (researchers, farmers, development agents, and students) capacity strengthening through vocational and academic training, and (3) Using CI for livelihood planning at all scales. These three factors support more effective identification and testing of agricultural technologies and practices addressing climate variability and change at plot, community, and landscape levels. This paper discusses the PAR-CSA methodology and parameters for evaluation, including biophysical and social change. Keys to success, including communication, knowledge sharing tools, and scalability are also discussed. Finally, future opportunities for improvement are presented, including knowledge product development, CSA policy and investment planning, capacity building, further engagement of the private sector, and additional research on existing practices and tools.

Traditional technologies for clay brick burning are often confronted with firing problems. The produced brick is of poor quality due to poor combustion inside the kiln. So that, cooking plays an important role because it determines the quality of the final product. Therefore, this work presents a transient computational fluid dynamic (CFD) study of firing clay bricks in the traditional oven. The heat transfer modeling in the furnace mainly is made from hydrodynamic equations and combustion reactions. Therefore, numerical simulation is done using ANSYS-Fluent software, using the standard k-ε turbulence model and the Eddy Dissipation turbulent combustion model. The results focused on the flow field, evolution of temperature, and O2 mass fraction to verify the combustion process inside the kiln. The results show that the kiln reaches the maximum temperature is in order of 900 °C and therefore verification process occurs. Moreover, the results revealed that the O2 mass fraction has the highest values at the inlet and it decreases gradually inside the kiln which gives good evidence about combustion process propagation.

AbstractClimate adaptation is an essential strategy for responding to climate change at local levels and required for sustainable food production to meet the growing food demand. In this light, this study analyzed the effects of climate adaptation strategies on technical efficiency of maize farmers in Northern Ghana. This involved a total of 619 maize farmers that were selected through a multistage sampling procedure. A Cobb-Douglas stochastic frontier was fitted to the data. From the result, the major climate adaptation strategies adopted by the farmers include row planting, changing planting date, mixed farming, refilling, and intercropping. The frontier result shows that while climate adaptation significantly leads to higher maize outputs, only crop rotation and row planting significantly improve technical efficiency of maize farmers. Other factors that significantly influence maize output are farm size, labor, seed, and chemicals. The study concludes that climate adaptation, particularly, crop rotation and row planting, remains essential adaptation strategies for sustainable food production in the region. However, further understanding of mechanisms through which majority of the climate adaptation strategies significantly reduce technical efficiency is required.