Industrial Water Treatment: New Trends, Challenges & Solutions
Bibliographic record
Abstract
This book documents the proceedings of the Industrial Water Treatment: Trends, Challenges, and Solutions symposium, which was sponsored by NACE International Committee STG 11 and held in Vancouver, British Columbia, Canada, from March 6 to 10, 2016, as part of CORROSION 2016. This book also includes papers presented at the Underdeposit Corrosion symposium, sponsored by NACE Committee TEG 092X. Recognized experts in their respective fields were invited to speak at the symposium, and there was particularly strong participation from representatives of academia, government, and industrial research. The symposia had two main objectives: (1) to provide a platform from which local and international participants could share the state of the art of their recent developments in water treatment chemicals, applications, challenges, and technologies, as well as to promote the exchange of information and knowledge between experts (industry and academic) and end users, and (2) to provide young researchers with professional guidance on the industrial trends, challenges, and focal areas for research in the water treatment industry and to encourage methodological rigor in research. This book covers the scientific and technological perspectives of industrial water treatment. The book’s 27 chapters are divided into three sections. Section A (Chapters 1 through 12) deals with the formation and inhibition of scale-forming salts—including calcium carbonate, calcium sulfate dihydrate, calcium oxalate, silica, and calcium phosphate—as well as the removal of impurities and the dispersion and stabilization of suspended matter in cooling and desalination systems. Section B consists of four chapters and focuses on the fundamentals of corrosion and its control in industrial water systems. Section C comprises 11 chapters (Chapters 17 through 27) and deals with scale formation, inhibition, challenges, and limitation in the oil and gas industry. The discussions cover conventional scale to exotic scale (e.g., iron sulfide) from conventional and unconventional resources, such as scaling in shale gas field. This book is intended primarily for scientists, researchers, technologists, and engineers working in the water treatment and water purification industries around the globe. Researchers in other industries—such as pharmaceutical, medical, and food—who are involved in the use of high-purity water, as well as those in academia, will also find useful information here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".