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The production and use of biochart and compost present many opportunities for soil improvement and agricultural productivity. However, the yield and performance of biochar depend on the feedstocks, pyrolysing temperatures and rate of application. Experiments were conducted to find out the effect of compost and biochar produced from two different feed stocks (Rice husk and Mexican sunflower) and pyrolysed at different temperatures (300, 350 and 400°C) on the growth, yield, nutrient uptake and chlorophyll contents of maize (Zea mays L.,). These were applied at three levels (5, 10 and 15 ton/ha) and the pots were laid out in a Completely Randomized Design (CRD) with four replicates. Data were collected on growth and yield attributes of maize, photosynthetic pigments and nutrient uptake by maize crop.The results showed that the feedstock pyrolyzed at temperature between 300 to 350°C and compost applied at higher rate between 10 to 15 ton/ha performed better. On the growth and yield parameters, compost and biochar at relatively low temperature and applied at 15 t/ha performed better than other treatments including control both at the main and residual experiments On the residual effect, the two types of biochar performed better than compost most especially sunflower biochar pyrolysed at 300 and 350°C and applied at 15 t/ha. The chlorophyll formation was enhanced more in maize treated with higher rates of biochar than lower rates. The result indicates that depending on feedstock, biochar and compost have potentials to serve as nutrient sources.

Thermal comfort is an important consideration in architectural design of modern building because of the implication on phgysiological impact of inhabitants. This study presents a nearnature learning strategy using Artificial Neural Networks (ANN) platform predicated on feedforward back propagation model to predict variation in air distribution on building's component to its thermal performance with consideration for energy management. Leverberg-Marquardt (LM) Algorithm was utilized to train the required location-specific geographical data in ANN module. Correlation coefficient and mean square error were used to validate the model. The results obtained with the trained data in neural network computing on thermal performance agreed very closely with those obtained in the analytical model used in the analysis with high correlation coefficient and minimal error metric was recorded for the mean square error. We study established the suitability of ANN-based prediction of thermal comfort and energy profiling in HVAC systems for near-nature effectiveness and performance of ventilation devices which may be applicable to residential and commercial buildings. The benefit of the ANN-based strategy presented in this study could be utilized for design of ventilation machines in eco-friendly buildings.

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Since the beginning of civilization, cooking has been done by using biomass as fuel. They are used in stoves which cause wastage of fuel and also health problems. Thus, there is the need to analyze the thermal performance of a developed cook stove that operates on multifuel conditions. The stove was designed to work on sawdust, wood, groundnut and charcoal as the primary fuel. Prior to fabrication, design parameters were obtained using the appropriate governing equations. Inputs were further made to simplify the construction of the stove and to minimize heat loss to the surroundings. A thermal efficiency of 32.18%, 80.10%, 38.73% and 50.33% was achieved when the stove was fuelled with charcoal, sawdust, wood and groundnut husk respectively. The highest flame temperature was recorded as 205ºC when wood was used as fuel. The highest stove body temperature recorded was 56ºC. Wood took the shortest time (20 mins) to boil water compared to sawdust, charcoal and groundnut husk which took 29, 23 and 27 minutes respectively for 2 kg of water. The developed cook stove was found to be energy efficient for domestic cooking especially in the rural communities of Nigeria.  Although it has the potential to save fuel, further research could be carried out in the aspect of removing CO emission.

Grinding (Particle size reduction) of biomass is an age-long operation that is performed during the preparation process of certain food products. Among the grinding mill machines mostly used for this operation are hammer mill and disk mill. Being that the nature of biomass affects the performance and choice of grinding-mill machine to be adopted, it is imperative to compare and select appropriate grinding mill machine that is efficient and effective. In this paper, a comparative technique to evaluate and select appropriate grinding mill machine for particle size reduction of dried white yam (Dioscorea rotundata) is proposed. Hammer mill and disk mill machines were selected for consideration. Two white yam species (Benue and Delta Yam) were prepared into dried chips and ground using the selected mills. Among the attribute (performance parameters) considered are crushing time, particle size distribution and energy consumed. A measure of performance (Index I) based on the comparative technique was formulated and used in evaluating the performance of the two mills. In the hammer mill, index I recorded 2721.2 and 3719.82 par/kWh for Benue Yam chips at screen size 4 and 6 mm, respectively, while 2647.89 and 3472.01 par/kWh was recorded for Delta yam chips at screen size 4 and 6 mm, respectively. Index I values for the Disk mill were 2536.25 and 2433.42 par/kWh at 1.2 mm clearance distance for Benue Yam chips and Delta Yam chips, respectively. The results indicated that hammer mill performed better overall than the disk mill. The comparative technique was found suitable in the evaluation of the performance of the mills. It is recommended that hammer mill be adopted.

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Aims: The performance of gas turbine is influenced by a number of factors which may be classified under three main headings: design limiting conditions, environmental dependent conditions and system respond condition. The design limiting conditions have been found to have influenced gas turbine performance most. In this study, two design limiting conditions (pressure ratio and maximum cycle temperature) were evaluated and estimated for optimal design and operations of gas turbine. Study Design: Hierarchical order in selecting the two design condition parameters was proposed. Methodology: Steady state energy and exergy concept was used to model the behavior of the plant. Results: Result obtained indicated that at maximum cycle temperature of 1173K, 1273K, 1373K, 1473K, 1573K, and 1673K,maximum pressure ratio for optimal performance were 19, 24, 29, 36, 44 and 51 respectively. Conclusion: These results provide insight into the performance behavior of gas turbine and also serve as a guide for operations and design optimization.