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The scheduling of crude oil processing for charging crude distillation unit (CDU) in petroleum refinery is a serious challenge especially when the CDU demand is uncertain. In this paper, we have shown that the problem of crude oil blending and inventory management scheduling under uncertainty can be cast in the framework of receding horizon control strategy. Fixed-end receding horizon is proposed and applied to a crude oil scheduling model to obtain schedules in a receding-like manner corresponding to different time steps over different prediction horizon lengths. Through case studies, this solution procedure was compared with the traditional scheduling solution approach and then with moving-end receding horizon strategy. Different disturbance scenarios are generated to evaluate the performance of the two receding horizon approaches in terms of efficiency to accommodate demand uncertainty.

In this paper, biological synthesis of silver nanoparticles (AgNPs) using Syzygium guineenses stem extract with 1mM, 2mM and 3mM AgNO3 concentrations has been presented. The plant extract was prepared with distilled water. The characterization and morphological composition of the synthesized AgNPs were determined by UV-visible spectroscopy and SEM respectively, while FTIR analysis was performed to identify the presence of the possible functional groups in the synthesized nano particles. It was observed from the UV and SEM analyses that the particles formed have diameters in the range of 23.5nm -89.3nm, which is the range of nanoparticle size. Antibacterial test was carried out on the sample with six pathogenic microbes (Methicillin Resistant Staphylococus aureas, Vancomycin Resistant Entrococci, Staphylococcus aureas, Bacillus sublitis, Escherichia coli, and Pseudomonas aeruginosa) to ascertain the antimicrobial activity of the synthesized AgNPs. Both the characterization and antimicrobial activity test were very successful and could lead to significant economic viability, as well as being environmentally friendly for treatment of some infectious diseases.

When a disturbance is introduced into a chemical plant, measurements are taken and control actions are implemented to compensate for the effects of the disturbance decades, several techniques for CV selections have been reported. Most of these techniques require process models to determine CVs offline and largely depend on the ability to obtained and a feedback input (manipulated variable) was derived. The performance of the proposed approach was tested using case stud disturbance with the base case model giving an optimal profit of $56,696,407 while the proposed approach yields $50,523,054, translating to 10.888 % loss. The percentage loss for the second, third and fourth cas respectively. The results obtained have shown that the idea presented was able to effectively deal with the situation at hand with percentage loss within a reasonable degree ABSTRACT Problem formulation as mixed integer nonlinear programming (MINLP) is one of the most challenging task in refinery scheduling optimization. In most of the work reported in refinery scheduling, uncertainties from design point of view predominate. However, t need to consider operational uncertainties (disturbances) as they affect the accuracy and robustness of the overall schedule. This study proposed a novel approach under optimizing control (SOC) framework under uncertain conditions. The goal is to maintain global optimum by controlling the gradient of the cost function at zero via approximating necessary conditions of optimality (NCO) over the whole uncertain parameter space. A regres revenue (profit) as a function of independent variables using optimal operation data was