Search NASA⌕ Search

DOE OSTI · 1984891

Integrated Electrocoagulation, Ultrafiltration, Membrane Distillation, and Crystallization for Treating Produced Water

Abstract

Produced water (PW) generated from hydraulic fracturing operations was treated using an integrated electrocoagulation, ultrafiltration, membrane distillation, and crystallization process (EC UF MDC). The aim was to determine the viability of this integrated process for maximizing water recovery. The results obtained here indicate that optimizing the various unit operations could lead to increased recovery of PW. Membrane fouling limits all membrane separation processes. A pretreatment step to suppress fouling is essential. Here, removal of total suspended solids (TSS) and total organic carbon (TOC) was achieved by electrocoagulation (EC) followed by ultrafiltration (UF). The hydrophobic membrane used in membrane distillation may be fouled by dissolved organic compounds. Reducing membrane fouling is essential to increase the long-term durability of the membrane distillation (MD) system. In addition, combining membrane distillation with crystallization (MDC) can help reduce scale formation. By inducing crystallization in the feed tank, scale formation on the MD membrane was suppressed. The integrated EC UF MDC process can impact Water Resources/Oil & Gas Companies. Conservation of surface and groundwater is possible by treating and reusing PW. Additionally, treating PW reduces the amount of PW disposed in Class II disposal wells and promotes more environmentally sustainable operations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jebur, Mahmood, Bachynska, Yelyzaveta, Hao, Xiaolei, Wickramasinghe, Sumith Ranil (ORCID:0000000174360517). 2023-06-12. Integrated Electrocoagulation, Ultrafiltration, Membrane Distillation, and Crystallization for Treating Produced Water. https://doi.org/10.3390/membranes13060597

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗