An investigation into the effects of combustible materials on the refractory properties of Ugwuoba clay, using sugarcane bagasse ash and corncob ash has been undertaken. Ugwuoba clay was sourced from Ugwuoba town in Oji River Local Government Area of Enugu State. Sugarcane bagasse were collected at Lokpanta, a Fulani settlement in Okigwe Community, Imo State, while corncobs were collected at New Artisan Market in Enugu Metropolis. The clay was processed using standard beneficiation and purification procedures at the Ceramics Department of Projects Development Agency (PRODA), Enugu. The sugarcane bagasse and corncobs were each and separately calcined into amorphous ash by heating in a furnace at 650oC. The refractory blends were compounded at the ratio of 90:10, 80:20, 70:30, and 60:40 for Ugwuoba clay (UGC) to Sugarcane Bagasse Ash (SBA) and Ugwuoba Clay (UGC) to Corncob Ash (CCA) separately and respectively. These blends were subsequently molded into the standard test pieces for the various properties determination and subjected to firing at temperatures of 900oC, 1000oC, 1100oC and 1200oC. Thereafter, the fired samples were characterized for fired shrinkages, total shrinkages, apparent porosities, water absorption coefficients, apparent densities, bulk densities and moduli of rupture. The results obtained for each of the blends showed that the values were within the tolerable limits for industrial refractories with the 10%SBA and the 20%CCA blends showing the best results. Comparatively however, the 10%SBA produced the better of these properties than the 20%CCA. A conclusion is drawn to the effect that both sugarcane bagasse ash and corncob ash can serve as good organic admixtures for refractory bricks production for the lining of melting furnaces in the metals industry, hence opening new frontiers for recycling of these agricultural wastes for environmental safety and economic development in Nigeria.

The pursuit of extending the driving range and improving the energy efficiency of electric vehicles (EVs) is a critical objective in advancing sustainable transportation. Central to this pursuit is the battery management system (BMS), which ensures the operational integrity and optimal performance of the EV's battery. Traditional BMSs have largely been conservative, relying on static parameters and predefined rules, which often do not fully exploit the battery's capacity or adapt to the dynamic nature of driving conditions. This has resulted in EVs that do not optimize their range, either underutilizing their energy or risking premature depletion. This paper introduces a dynamic scheduling approach for battery usage in EVs, a paradigm shifts from traditional BMS algorithms that are deterministic and linear, to one that is adaptable and predictive. The proposed dynamic scheduling method utilizes data analytics, machine learning, and real-time monitoring to anticipate and adapt to varying driving conditions, traffic patterns, driver behavior, and route topography. The objective is not just to respond to the battery's current state but to manage the energy distribution proactively, optimizing the use of the stored energy and enhancing the vehicle's range on a single charge. The paper explores the technological advancements enabling dynamic scheduling, the benefits of such a system, and the challenges it may encounter. It is posited that dynamic scheduling represents a necessary evolution in battery management, capable of significantly boosting the range and desirability of EVs. Finally, the paper proposes a novel system and method that leverage real-time data and machine learning to implement an effective energy management strategy for dynamic scheduling, which could markedly improve the range of an EV. The implications of this approach suggest a future where EVs can meet and exceed the range expectations of consumers, thereby accelerating the transition to electric mobility.