In this research, hydrogel biocomposites were prepared from whey protein isolate (WPI), reduced graphene oxide (rGO), and synthetic polymers in varied ratios. Their physicochemical properties were evaluated by FTIR, SEM, TGA, AFM, and TEM. FTIR spectra revealed significant peaks at 1167 cm −1 for C-O-C peak and at 1449 cm −1 for O-H bending for WPI and rGO, respectively. The hydrogels were loaded with proguanil hydrochloride and chloroquine diphosphate and in vitro release kinetics of individual drugs from the biocomposites were studied. The SEM images of the biocomposites after drug release confirmed that they are biodegradable. The drug release was controlled, pH-dependent which further confirmed that the hydrogels are pH-sensitive. The release of proguanil from the hydrogels was slow when compared to chloroquine, suggesting that the solubility of the drug influenced their rate of release. The drug release from the biocomposites fitted the Korsmeyer-Peppas model with n values for chloroquine between 0.46 and 0.49 at pH of 1.2 and between 0.72 and 1.41 at pH of 7.4. The n values for proguanil were between 0.66 and 0.83 at pH 1.2 and 0.85-0.92 at pH 7.4. The results obtained suggested that the biocomposites are potential systems that can be tailored for controlled delivery of bioactive agents.

No abstract

No abstract

Waterflooding is a recovery technique where water is pumped into an oil reservoir for increase in production. Changing reservoir states will require different injection and production settings for optimal operation which can be formulated as a dynamic optimization problem. This could be solved through optimal control techniques which traditionally can only provide an open-loop solution. However, this solution is sensitive to uncertainties which is inevitable to reservoirs. Direct feedback control has been proposed recently for optimal waterflooding operations with the aim to counteract the effects of reservoir uncertainties. In this work, a feedback approach based on the principle of receding horizon control (RHC) was developed for waterflooding process optimization. Application of RHC strategy to counteract the effect of uncertainties has yielded gains that vary from 0.14% to 19.22% over the traditional open-loop approach. The gain increases with introduction of more uncertainties into the configuration. The losses incurred as a result of the effect of feedback is in the range of 0.25%-15.21% in comparison to 0.39%-31.51% for the case of traditional open-loop control approach. ARTICLE HISTORY

In this paper, static and dynamic optimization of a reservoir waterflooding process for enhanced oil recovery was studied. The dynamic optimization was achieved using receding horizon (RH) algorithms. Two forms of RH which are movingend and fixed-end RH were formulated and compared. MATLAB Reservoir Simulator (MRST) from SINTEF was used for reservoir simulation. The objective function to be maximized is net present value (NPV) of the venture while the control variable is water injection rate. Sequential quadratic programming (SQP) was applied for the optimization. It was found out that fixed-end RH gave the highest NPV with improvements of 0.81% and 1.49% over static and moving-end RH strategies respectively. Keywords-dynamic optimization; static optimization; movingend receding horizon; fixed-end receding horizon; waterflooding process, enhanced oil recovery I.

A dip-coating technique was applied to prepare a selective membrane on a commercial ceramic mesoporous support. Single gas components used for permeance and selectivity were CH4, CO2, H2, He, N2, and O2(BOC UK) with at least 99.999 (% v/v) purity. The permeances and selectivities were obtained at room temperature and transmembrane pressure differences between 0.05 up to 5.0 barg. Gas permeation experiments showed the permeance of CO2to be strongly influenced by surface diffusion mechanism. Single gas experiment showed linear flow dependence on the inverse square root of molecular weight at room temperature and 1.0 barg. The single gas selectivities were found to be higher than the ideal Knudsen separation mechanism. The highest CO2/CH4selectivity value of 24.07 was obtained at 0.7 barg and room temperature.

No abstract

Self-optimizing control (SOC) is a technique used in selecting controlled variables (CVs) for a process plant control structure with a view to operating the plant optimally in the presence of uncertainties and disturbances. Existing SOC approaches are either local which result to large losses or too cumbersome to be applicable to real systems. In this work, a novel method of CV selection based on data was developed. In the method, a compressed reduced gradient of a constrained optimization problem was proposed to be estimated using finite difference scheme. The CV function was then used to approximate the necessary condition of optimality (NCO) using data only in a single regression step. The new approach was applied to a simplified case study and its performance was compared to an existing SOC methodology. An excellent goodness of fit was obtained during 2 the regression with a R -value of 1.0 associated with one of the designed CVs. The formulated CVs were found to be very robust with performance similar to that of NCO approximation method. A zero loss was incurred with one of the CVs.Keywords: compressed reduced gradient, constrained problems, controlled variable, data-driven, necessary condition of optimality, self-optimizing control.yi4cao2@gmail.com ABSTRACT 273