Self-optimizing control methodology for mixed integer programming problems: a case study of refinery production scheduling

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, there is also a need to consider operational uncertainties (disturbances) as they affect the accuracy and robustness of the overall schedule. This study proposed a novel approach under self- optimizing control (SOC) framework to deal with multi-period refinery scheduling problems 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 regression model for the plant expected revenue (profit) as a function of independent variables using optimal operation data was obtained and a feedback input (manipulated variable) was derived. The performance of the proposed approach was tested using case studies. The first case assumed a system with no 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 cases with disturbances are 5.807 %, 4.409% and 7.898% 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 degreeKeywords: Refinery scheduling scheduling, MINLP formulation, Operational uncertainty (disturbances), Necessary condition of optimality, Feedback contro

Biosynthesis, characterization and antimicrobial study of silver nanoparticles (agNPs)

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.Keywords: Syzygium guineenses, Green Chemistry, Spectroscopy, Optoelectronics, Biomedical Sensor

Determination of morphological features and molecular interactions of Nigerian bentonitic clays using Scanning Electron Microscope (SEM)

This research focused on identifying the morphological features and molecular  interactions of the Nigerian Bentonitic clays using Scanning Electron Microscope (SEM) characterisation technique. The SEM microstructure images indicated that the bentonite samples are generally moderately dispersive to dispersive with some large flocs which were apparently separate and dispersed from one another rather than located on the totality of the image. This property is more pronounced on the sodium (Na) activated and the treated samples than in the raw samples, which could be due to quartz removal and Na activation on the raw Bentonitic samples. Dispersive sample surfaces consist of loose flakes with no definitive mass structures observed on the treated samples due to added poly anionic cellulose (PAC) to the samplesKey words: SEM, microstructure, images, bentonite, flocs and PA