Price Control 101: For Chemical Companies

Petrochemical plants and refineries both process hydrocarbon derived materials. Refineries focus on fuels production. Refinery products include LPG (liquefied petroleum gas), gasoline (petrol), kerosene and jet fuel, diesel, fuel oils, and coke. Petrochemicals focus on creating intermediates used to produce industrial and consumer products. Major petrochemical processes include steam cracking to produce ethylene (ethane), propylene (propene), and butadiene and aromatics plants producing benzene, toluene, xylene and other aromatics.

The management of hazardous waste in China is becoming a more and more important topic as the central government has been putting a lot of effort in the regulation on the hazardous chemicals and the improvement of the environmental quality. The latest version of the Inventory of Hazardous Wastes was published in March 2016. The new version became effective on Aug 1st 2016 and included significant changes compared to its predecessor, the 2008 version. However, industry has encountered some problems relating to the implementation of the Inventory. So ChemLinked invited Ms. Amy Tu to give our readers a comprehensive introduction to the management of hazardous waste in China.

Industry is currently facing a substantial challenge. There is an increasing need to develop chemical hazard data for both new and existing chemicals, while at the same time there are current and proposed regulatory mandates as well as ethical drivers to reduce, refine, and replace (the “3 R’s”) the use of animal test methods with alternative non-animal methodologies. This creates a challenge for innovators, formulators, and companies who strive to introduce a new chemical or fill data gaps on existing chemicals while adhering to the principles of the 3 R’s. In silico, or computational toxicology software offers an alternative method for predicting chemical hazards. Traditional Quantitative Structure Activity Relationship (QSAR) methodology employs simple analog identification and predicting hazard data from chemical analogs with known hazard data. This webinar will cover the UL Cheminformatics Tool Kit, a novel in silico approach combining big data with advanced machine learning incorporating data fusion. The approach integrates novel models called RASARs (Read-across Structure Activity Relationship) with data fusion techniques that extend this concept by considering all available property data on both the target and analogs rather than only the modeled hazard.

In cheminformatics today, innovations such as IUPAC’s InChI have created an explosion of interconnectedness for chemical structure databases. Unfortunately, chemists continue to draw 2D structures containing implicit 3D information (chair and boat, Haworth or Fischer projections, etc.). To address the issue, chemical information scientists use software-based ‘standardisation’ of chemical structures and in the process may remove or misinterpret the implicit stereochemistry of the original structure. In this webinar, we will explore an alternative, more accurate software-based ‘interpretation’ method that understands the 3D intent of 2D drawings as a chemist would. Case studies comparing both methods will be presented.