Jurnal Teknik Indonesia

Driving Sustainability in Power Generation: Amine Scrubbing Integration as a Cost-Effective Measure for Carbon Dioxide Mitigation

2026-01-02T07:54:23+00:00

The imperative of energy security, sustainability, and independence necessitates the continued use of coal for electricity generation. However, to mitigate rising CO₂ levels, developing carbon capture and storage (CCS) technologies is crucial. This study explores various approaches to optimize CO₂ capture using chemical solvents, focusing on reducing the energy demands of solvent regeneration. Our analysis reveals that the current cost of CO₂capture stands at approximately #55,000.000 (Naira) per ton of CO₂ , with a target to reduce it to below #25,000.000,00 (Naira) per ton of CO₂ . We evaluate the technical and economic performance of different approaches, calculating the specific cost per ton of CO₂ captured. Our results show that:- Current Cost: #55,000.000,00 (Naira) per ton of CO₂ - Target Cost: Below #25,000.000,00 (Naira) per ton of CO₂ - Efficiency Reduction: Minor efficiency reductions observed in some proposed schemes

Exploring the Utilization of Plastic Sand in Construction to Drive Sustainable Practices and Foster a Circular Economy

2026-01-07T09:25:39+00:00

This research explores utilizing waste plastics in construction to promote a circular economy and mitigate plastic waste's environmental footprint in Nigeria's urban centers. Innovative bricks made from low-density polyethylene bags and sand offer thermal and sound insulation properties, pollution control, and cost reduction. The eco-friendly bricks demonstrate beneficial properties: lightweight, porous structure, low thermal conductivity, and high mechanical strength comparable to conventional bricks.The experimental process involved combining cement, sand, and waste plastic/nylon (0-15% by weight) with fly ash, followed by underwater curing and baking. Results show compressive strengths of 19.5 MPa (0% waste), 19.46 MPa (5% waste), 20.3 MPa (10% waste), and 21.1 MPa (15% waste). The bricks exhibit reduced water absorption capacity (0.085-0.34%) and lower efflorescence values.The innovative bricks offer numerous benefits: enhanced strength and durability, reduced environmental impact, and economic value for manufacturers. They contribute to sustainable construction practices, reducing sand extraction and plastic waste accumulation. This research showcases the potential for eco-friendly bricks to enhance energy efficiency in buildings and promote a sustainable ecosystem for plastic waste management.

Optimizing Oil Well Cementing: Effects of Dispersant and Fluid Loss Additive Concentrations on Thickening Time and Free Fluid Formation

2026-01-12T15:58:05+00:00

Optimising cement slurry properties through appropriate additive selection is critical for ensuring successful zonal isolation and preventing costly wellbore failures in oil and gas operations. This research investigated the effects of dispersant and fluid loss additive concentrations on the thickening time and free fluid formation of Class G cement slurry. A systematic two-factor three-level (3²) factorial design was employed, generating nine experimental runs. Statistical analysis included correlation analysis, ANOVA, and multiple regression modelling. The results revealed that dispersant concentration exhibited the strongest influence on thickening time behaviour, establishing it as the primary control mechanism for cement slurry pumpability. Fluid loss additive concentration demonstrated dual functionality, serving both as an effective filtration control agent and providing secondary influence on thickening time. Significant interaction effects were observed between dispersant and fluid loss additive concentrations (p = 0.040), indicating synergistic behaviours. Multiple regression analysis yielded highly predictive models for both response variables, with R² = 0.981 for thickening time and R² = 0.845 for free fluid. The developed models provide a systematic framework for cement slurry optimisation in oil well cementing operations, enabling precise targeting of cement slurry properties for improved well integrity and performance. The research demonstrates the effectiveness of factorial experimental design methodology for understanding complex additive interactions in cement slurry systems.

Evaluating the Impact of Dispersants and Fluid Loss Additives on Cement Slurry Thickening Time: Experimental Design and Statistical Modeling

2026-01-18T15:20:11+00:00

Optimising cement slurry properties is critical for successful zonal isolation and preventing wellbore failures in oil and gas operations. This research investigated the effects of dispersant (0.1-1.0% BWOC) and fluid loss additive (0.1-0.35 gal/sk) concentrations on thickening time and free fluid formation of Class G cement slurry using a 3² factorial design. Thickening time was measured with an atmospheric consistometer; free fluid via HTHP filter press. Analysis included ANOVA and multiple regression modeling. Key findings Dispersant strongly influences thickening time (R² = 0.981, RMSE = 8.731 min). Fluid loss additive controls filtration and moderately affects thickening time. Significant dispersant-fluid loss additive interaction (p = 0.040) indicates synergistic effects. Models enable predictive optimization of cement slurry properties

Evaluation of Orange Fruit Quality Clustering Using a Real-Time X-Means Algorithm

2026-01-30T02:42:46+00:00

The citrus farming industry faces major challenges in maintaining product quality consistency due to subjective manual sorting processes that are prone to fatigue and have varying standards. This problem results in economic losses due to errors in detecting ripeness levels and physical damage that hinders market competitiveness. This study aims to design and implement an automated citrus fruit quality evaluation system using a real-time X-Means algorithm. The research method begins with visual data acquisition through a camera sensor using the automatic snapshot feature to convert physical objects into digital data. The data then undergoes preprocessing, which includes filtering to remove noise, color (RGB) and texture feature extraction, and normalization using Min-Max Scaling to balance parameter weights. The X-Means algorithm is used because of its ability to independently determine the optimal number of clusters through the evaluation of the Bayesian Information Criterion (BIC) score. The processing results show that the system is able to accurately group oranges into three categories: ripe, which are dominated by bright orange colors; unripe, which are dominated by green colors; and rotten, which are identified through rough textures and dull colors. The integration of this technology ensures that all decision-making occurs quickly and objectively, providing a practical solution for the industry to consistently improve product quality control efficiency in the field.