Waqas Aleem

The conversion of biomass into biofuels is a crucial step towards sustainable energy transitions. These processes are multi‐scale phenomena that require advanced computational modeling. This study synthesizes bibliometric analysis and visualization to examine the scientific advancements and future trends in computational modeling for biomass‐to‐biofuel transformation. To identify thematic clusters, research hotspots, and evolving collaborations from 2003 to 2025, network mapping and keyword co‐occurrence analysis were conducted on a dataset of 3,689 Scopus‐indexed publications. Publication trends were analyzed using VOSviewer and Microsoft Excel, highlighting the most active authors, institutions, countries, journals, and collaboration networks. The findings reveal exponential growth in research after 2017, accompanied by an increasing focus on response surface methodology (RSM), machine learning, CFD, and hybrid modeling approaches. China leads in publication output, while the United States records the highest citation impact, with Tianjin University achieving the greatest productivity and National Cheng Kung University the highest citation‐per‐publication ratio among institutions. Recent research increasingly focuses on hybrid physics‐informed and data‐driven models, multi‐scale coupling, high‐performance computing, and their integration with techno‐economic and life‐cycle assessments for sustainability. These trends outline the path for further development of computational strategies, with implications for scalability, cost efficiency, and environmentally sustainable biofuel production.

Accurate estimation of formation conditions plays a pivotal role in effectively managing various processes related to hydrates, including flow assurance, deep-water drilling, and hydrate-based technology development. The formation temperature of methane hydrates in the presence of brine greatly affects the efficacy and accuracy of these processes. This work presents a comprehensive and novel comparative analysis of nine distinct machine learning models for accurate prediction of formation temperatures of methane hydrate. This study investigated the application of major machine learning (ML) algorithms including multiple linear regression (MLR), long short-term memory (LSTM), radial basis function (RBF), support vector machine (SVM), artificial neural network (ANN), gradient boosting regression (GBR), gradient process regression (GPR), random forest (RF), and K-nearest neighbor (KNN). The model accuracy was validated against a large dataset comprising of over 1000 data points with diverse range of salt concentrations. In this regard, model accuracies were compared using several metrics including R², ARD, and AARD. The experimental results exhibited KNN algorithm to be fast-converging, accurate, and consistent over the entire range of data points with an R² score of 0.975 and AARD of 0.385%. The results enable efficient and accurate temperature estimation with ML algorithms for multiple hydrate-related processes.

Performance evaluation of drilling fluids is essential for a successful drilling project, as they not only remove drill cuttings but also prevent undesired penetration or outflow of formation fluids by sealing off wellbore walls. However, concerns have been raised about the use of chemical additives in drilling fluids due to their toxicity and non-biodegradability. To this end, agricultural waste materials are recognized as a promising alternative as they are cost-effective, environmentally sustainable, and can be used as a substitute for lost circulation materials. Rice husk ash (RHA) has become popular as an additive due to its renewable characteristics, including its large surface area, silica content, and microporous structure. This research article explores the rheological properties of drilling fluid with RHA as a filter control medium. The results showed that increasing concentrations of RHA in the drilling mud significantly improved its rheology, particularly at higher concentrations (15 and 20 wt.%). The addition of RHA modified the filtration and rheological properties of the drilling mud, resulting in improved plastic viscosity, yield point, density, gel strength, and thixotropy. However, filter loss and mud cake thickness increased at elevated RHA concentrations. Furthermore, the pH test revealed that the mud's properties shifted toward the acidic region as the RHA concentration increased. The results indicate that RHA could be used as a sustainable and cost-effective alternative to conventional chemical additives with a positive environmental impact. This study may also provide valuable insights into the use of RHA in water-based bentonite mud and could serve as a guide for future research in the drilling industry. Graphical abstract

Performance evaluation of drilling fluids is eminent for a successful drilling project as they not only remove drill cuttings but also prevent undesired penetration or outflow of formation fluids by sealing off wellbore walls. However, concerns have been raised about the use of chemical additives in drilling fluids due to their toxicity and non-biodegradability. To this end, agricultural waste materials are recognized as a promising alternative as they are cost-effective, environmentally sustainable, and can be used as a substitute for lost circulation materials. Rice husk ash (RHA) has become popular as an additive due to its renewable characteristics including large surface area, silica content, and microporous structure. This research article explores the rheological properties of drilling fluid with RHA as a filter control medium. The results showed that increasing concentrations of RHA in the drilling mud significantly improved its rheology particularly at higher concentrations (15 and 20 wt.%). The addition of RHA modified the filtration and rheological properties of the drilling mud resulting in improved plastic viscosity, yield point, density, gel strength, and thixotropy. However, the filter loss and mud cake thickness increased at elevated RHA concentration. Furthermore, the pH test revealed that the mud's properties shifted towards the acidic region as the RHA concentration increased. The results indicate that RHA could be used as a sustainable and cost-effective alternative to conventional chemical additives with a positive environmental impact. This study may also provide valuable insights into the use of RHA in water-based bentonite mud and could serve as a guide for future research in the drilling industry.

With the application of chemical enhanced oil recovery methods, water separation is a major issue in a production facility. Oil/water separation is suppressed with a stable emulsion. The present study evaluated the impact of different emulsifiers in enhanced oil recovery. The effectiveness of each additive such as an alkali, surfactant, or polymer on the stability of the emulsion was anticipated using laser scattering to measure the emulsion’s stability. An artificial neural network was applied to predict the effectiveness of the additives on stabilization/destabilization and to assess how alkali/surfactants, surfactant/polymers, and polymer/surfactants affect the separation profiles. Measurements of the viscosity and zeta potential of residual emulsion clarify that the increase in surfactant makes the emulsion stable and became unstable with the increase in the alkali and polymer. The droplet zeta potential was within −i9~−i5 mV. The absolute value of the zeta potential decreased at a high polymer concentration with a low surfactant concentration resulting in fast flocculence phenomena. With an increase in the surfactant concentration and the presence of high alkali, the droplet’s absolute zeta potential demonstrated an increase in the repulsion force in the emulsions. The study also focuses on the carbon number distribution, wax appearance temperature (WAT) and wax content of four crude oil samples from different field locations (Miri, Angsi, Penara and Dulang). Findings show that crude oil samples with higher mol percent of carbon distribution from C₂₀ to C₄₀ (paraffinic composition) contains higher wax content, wt% and subsequently results in higher wax appearance temperature (WAT). This is obviously shown by the crude oil sample from Penara field. Further similar investigation on other field locations will assist in characterizing the paraffinic composition in Malaysian oil basins.

Alkalis, surfactants and/or polymers are usually injected in the injector wells so that the injection fluids can sweep through the reservoir and mobilize/recover more oil. However, the formation of stable emulsion due to the residual chemicals in the recovered crude oil poses problems in the primary separation process. This study focuses on the effect of Alkali-Surfactant-Polymer injection on creaming and coalescence of crude oil and water emulsion produced in the primary gravity separator. In addition, a model is developed in this work to predict the separation effectiveness of oil and water emulsion containing ASP. In this work, the effect of ASP on the stability and the separation of crude oil water emulsion is investigated using the Turbiscan light transmission and back scattering device. The range of concentration of ASP used in this work is same as that found in the primary gravity separator. The investigation revealed that ASP plays an important role in the stability of the emulsion. A new model based on Stokes law and incorporating coalescence phenomena for the prediction of separation profile of oil and water in terms of separated layer profile is developed. The model is validated with experimental data obtained from the Turbiscan light transmission and backscattering device. The model is able to predict the separation profile of an oil-water emulsion containing ASP with excellent agreement. (a) Oil emulsion in a batch separator (b) Separation effectiveness of oil in water emulsion in terms creaming and coalescing interfaces in water height with time t.

A new relation between viscosity and surface tension is proposed and checked for 21 pure hydrocarbon liquids, and validated with the National Institute of Science and Technology data. The performance and accuracy of the proposed relation are compared with the reported empirical correlations in the form of absolute average deviation. The proposed relation remains valid for higher hydrocarbons (up to C₂₅H₅₂). A total of 418 data points for 21 pure hydrocarbon liquids were taken into consideration for checking the accuracy of the proposed relation. The proposed relation gives an absolute average deviation of 5.66%, indicating high accuracy of the proposed relation.

This paper proposed a model that explains the separation mechanism of oil-in-water emulsion taking into account both creaming and coalescence processes. Oil-in-water emulsion separation experiments were performed in a batch separator using kerosene and distilled water. The predictions of the proposed model agreed well with the experimental results as well as previously published experimental data. The comparison between the proposed model and the previously published model showed that the proposed model has higher accuracy in predicting the separation profile of oil-in-water emulsion, with an accuracy of within 10%. Thus the proposed model gives better representation of the oil-in-water emulsion separation process.