Effect of bacterial cellulose nanofibers incorporation on acid-induced casein gels: microstructures and rheological properties

In this study, the effect of bacterial cellulose nanofibers (BCNF) incorporation on the structural and rheological properties of casein gels was investigated, where the mixed BCNF and casein gels were prepared by adding gluconic acid δ-lactone (GDL) to acidify the mixed polymer solutions at 3.0% casein concentration (w/v) and varying BCNF concentrations (0–0.5%, w/v). By changing the addition amount of GDL, the mechanical and structural properties of the mixed gels were studied at above, near and below the electric point (pI) of the casein. At pH above the pI of the casein, the introduction of BCNF initially increased the gel strength, but further addition of BCNF weakened the mixed gels. At near and below the pI of the casein, the incorporation of BCNF continuously increased the gel strength. Besides, all gels showed good structural homogeneity, without macroscopic phase separation occurring, which indicated good compatibility of BCNF with the casein gels.

Optimization of novel single-step gelatin extraction method using RSM model for valorization of surimi industry by-products

Surimi industry produces large quantity of by-products as a combination of skin, bones, and scale, which due to technical difficulty in separation, are being currently utilized for production of low- value products such as biofertilizers and fish feed. Present paper focuses on utilization of combined skin, bones, and scale from Pink Perch (Nemipterus japonicus) obtained from surimi industry for gelatin extraction using single step process. Single step extraction method with acetic acid and water was optimized using Response Surface Methodology (RSM) to maximize yield and gel strength so that the process can be applied for sustainable utilization. Parameters such as pH (A), extraction temperature (B) and extraction time (C) with respect to yield and L-hydroxyproline content were optimized. Highest gelatin yield was obtained at pH 3, 75°C extraction temperature, and 30 min extraction time. Gelatin yield and L-hydroxyproline content under optimum condition were 16.2% and 41.62 mg.g−1. The chemical composition, functional, rheological, and structural properties of gelatin were examined and compared with commercial bovine gelatin. Gelatin thus obtained at optimized condition exhibited high gel strength (793g) and higher imino acid content (18.1%) than bovine gelatin. FTIR spectra depicted high similarities between both gelatin sample. Thus, the optimized method can be utilized for gelatin extraction from Pink Perch by-products for development of high value products such as food application.

Alternative Use of Achi, UKPO, and OFOR as Substitute to Standard Viscosifier

In Nigeria, drilling companies import a bulk of drilling fluid materials that they use to carry out their respective operations. This has been a major concern to oil and gas industries since these drilling fluid materials cannot be recycled, are highly expensive in terms of foreign exchange, are not environmentally friendly, not very effective, and non-biodegradable. This work presents an experimental investigation into the reliability of the use of local materials as a substitute to conventional viscosifiers. Local materials used in the analysis are Mucuna solannie (Ukpo), Brachystegia eurycoma (Achi), and Detarium microcarpium (Ofo). The results obtained from the experimental analysis show that they compared closely to the standard viscosifer formulated with Pac-R. The results showed that the density, specific gravity, pH, yield stress, Gel strength, Plastic Viscosity, and yield point of mud formulated from local materials compared favorably with that of the imported viscosifer. It was observed that an increase in concentration produced a better result. Hence, they could replace the Pac-R considering cost, cutting carrying ability, etc.

Influence of the Mixtures of Vegetable Oil and Vitamin E over the Microstructure and Rheology of Organogels

Candelilla wax (CW) and 12-hydroxystearic acid (12HSA) are classic solid-fiber-matrix organogelators. Despite the high number of studies using those ingredients in oily systems, there is scarce literature using a mixture of oil and antioxidants. Vitamin E (VE) is an important candidate for its lipophilicity and several applications on pharmaceutical, cosmetics, and food industries. In this work, we investigated the influences of mixtures between vegetable oil (VO) and VE on the microstructures and rheological properties of CW and 12HSA organogels. A weak gel (G′′/G′ > 0.1) with a shear-thinning behavior was observed for all samples. The presence of VE impacted the gel strength and the phase transition temperatures in a dose-dependent pattern. Larger and denser packed crystals were seen for 12HSA samples, while smaller and more dispersed structures were obtained for CW organogels. The results obtained in this work allowed the correlation of the structural and mechanical properties of the organogels, which plays an important role in the physical-chemical characteristics of these materials.

Pathway of sodium alginate synthesis from marine brown algae, Sargassum wightii from Sri Lanka

Alginates are natural polysaccharides that are extracted from brown seaweed varieties and it is widely used for their rheological properties. The main step in the extraction protocol of sodium alginate is alkaline extraction. Sodium alginate was produced by dipping the seaweed in 1% formaldehyde and 2.5% of sodium carbonate solution and properties were studied following the standard methods. The amount of sodium alginate yield was 31.7% in Sargassum wightii. The moisture content and the ash content were recorded at 16.82% and 5.20%, respectively. The viscosity and the gel strength were noted as 40 vcP, 4.54 × 10–2 kN with 0.1 M CaCl2 and 6.86 × 10–2 kN with 0.2 M CaCl2 respectively. This study of the extraction method and its properties reveal that Sargassum wightii brown seaweed species have a high affinity to extract the alginate.

Characterization based machine learning modeling for the prediction of the rheological properties of water-based drilling mud: an experimental study on grass as an environmental friendly additive

The successful drilling operation depends upon the achievement of target drilling attributes within the environmental and economic constraints but this is not possible only on the basis of laboratory testing due to the limitation of time and resources. The chemistry of the mud decides its rheological potential and selection of the techniques required for recycling operations. Conductivity, pH, and photometer testing were performed for the physio-chemical characterization of the grass to be used as an environmental friendly drilling mud additive. In this study, different particle sizes (75, 150, and 300 µm) of grass powder were mixed in mud density of 8.5, 8.6, and 8.7 ppg in the measurement of gel strength and viscosity of drilling mud. The grass additive was added in different weight conditions considering no additive, 0.25, 0.5, and 1 g to assess the contribution of grass on the gel strength and viscosity of the drilling mud. The machine learning techniques (Multivariate Linear Regression Analysis, Artificial Neural Network, Support Vector Machine Regression, k-Nearest Neighbor, Decision Stump, Random Forest, and Random Tree approaches) were applied to the generated rheological data. The results of the study show that grass can be used for the improvement of the gel strength and viscosity of the drilling mud. The highest improvement of the viscosity was seen when grass powder of 150 µm was added in the 8.7 ppg drilling mud in 0.25, 0.5, and 1 g weights. The gel strength of the drilling mud was improved when the grass additive was added to the drilling mud 8.7 ppg. Random forest and Artificial Neural Network had the same results of 0.72 regression coefficient (R2) for the estimation of viscosity of the drilling mud. The random tree was found as the most effective technique for the modeling of gel strength at 10 min (GS_10min) of the drilling mud. The predictions of Artificial Neural Network had 0.92 R2 against the measured gel strength at 10 s (GS_10sec) of the drilling mud. On average, Artificial Neural Network predicted the rheological properties of the mud with the highest accuracy as compared to other machine learning approaches. The work may serve as a key source to estimate the net effect of grass additives for the improvement of the gel strength and viscosity of the drilling mud without the performance of any large number of laboratory tests.

Role of Sodium Dodecyl Sulfate in Tailoring the Rheological Properties of High-Strength Gelatin Hydrogels

Gelatin hydrogels are widely used materials that may require surfactants to adjust their solution’s surface tension for cell attachment, surface adsorption enhancement, or foaming. However, gelatin is a highly surface-active polymer, and its concentrated solutions usually do not require surfactants to achieve low surface tension. However, anionic surfactants, such as sodium dodecyl sulfate (SDS), interact strongly with gelatin to form complexes that impact its hydrogels’ rheological properties, influencing processability and functionality. Nevertheless, there is a lack of systematic research on the impact of these complexes on high gelatin content (i.e., high strength) hydrogels’ rheological properties. In this work, the SDS/gelatin ratio-dependent viscoelastic properties (e.g., gel strength, gelation kinetics, and melting/gelling temperature) of high-strength gelatin hydrogels were investigated using rheology and correlated to surface tension, viscometry, FTIR, and UV-Vis spectrophotometry. SDS–gelatin ratio was proved to be an important factor in tailoring the rheological properties of gelatin hydrogels. The gel strength, gelation kinetics, and melting/gelling temperature of the gelatin hydrogels linearly increased with SDS incorporation up to a maximum value, from which they started to decline. The findings of this work have wide applicability in tailoring the properties of gelatin–SDS solutions and hydrogels during their processing.

Development of New Models to Determine the Rheological Parameters of Water-based Drilling Fluid Using Artificial Intelligence

It is well known that drilling fluid is a key parameter for optimizing drilling operations, cleaning the hole, and managing the rig hydraulics and margins of surge and swab pressures. Although the experimental works present valid and reliable results, they are expensive and time consuming. On the other hand, continuous and regular determination of the rheological mud properties can perform its essential functions during well construction. More uncertainties in planning the drilling fluid properties meant that more challenges may be exposed during drilling operations. This study presents two predictive techniques, multiple regression analysis (MRA) and artificial neural networks (ANNs), to determine the rheological properties of water-based drilling fluid based on other simple measurable properties. While mud density (MW), marsh funnel (MF), and solid% are key input parameters in this study, the output functions or models are plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength. The prediction methods were demonstrated by means of a field case in eastern Iraq, using datasets from daily drilling reports of two wells in addition to the laboratory measurements. To test the performance ability of the developed models, two error-based metrics (determination coefficient R2 and root mean square error RMSE) have been used in this study. The current results of this study support the evidence that MW, MF, and solid% are consistent indexes for the prediction of rheological properties. Both mud density and solid content have a relative-significant effect on increasing PV, YP, AV, and gel strength. However, a scattering around each fit curve is observed which proved that one rheological property alone is not sufficient to estimate other properties. The results also reveal that both MRA and ANN are conservative in estimating the fluid rheological properties, but ANN is more precise than MRA. Eight empirical mathematical models with high performance capacity have been developed in this study to determine the rheological fluid properties based on simple and quick equipment as mud balance and marsh funnel. This study presents cost-effective models to determine the rheological fluid properties for future well planning in Iraqi oil fields.