Search results for: Nutcha Thianbut

Authors: Nutcha Thianbut

Abstract:

In the petroleum drilling industry, besides oil and gas, water is also produced from petroleum production. which will have oil droplets dispersed in the water as an emulsion. Commonly referred to as produced water, most industrial water-based produced water methods use the method of pumping water back into wells or catchment areas. because it cannot be utilized further, but in the compression of water each time, the cost is quite high. And the survey found that the amount of water from the petroleum production process has increased every year. In this research, we would like to study the removal of oil in produced water by the Coalescer device using fibers from agricultural waste as an intermediary. As an alternative to reduce the cost of water management in the petroleum drilling industry. The objectives of this research are 1. To study the fiber pretreatment by chemical process for the efficiency of oil-water separation 2. To study and design the fiber-packed coalescer device to destroy the emulsion of crude oil in water. 3. To study the working conditions of coalescer devices in emulsion destruction. using a fiber medium. In this research, the experiment was divided into two parts. The first part will study the absorbency of fibers. It compares untreated fibers with chemically treated alkaline fibers that change over time as well as adjusting the amount of fiber on the absorbency of the fiber and the second part will study the separation of oil from produced water by Coalescer equipment using fiber as medium to study the optimum condition of coalescer equipment for further development and industrial application.

Keywords: produced water, fiber, surface modification, coalescer

Procedia PDF Downloads 134

Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

Abstract:

Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

Procedia PDF Downloads 41