Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Methods and models in process safety and risk management: Past, present and future

https://aiche.onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16489

The promise of artificial intelligence in chemical engineering: Is it here, finally?

Artificial intelligence in drug discovery and development.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jps.24594

The Future of Pharmaceutical Manufacturing Sciences.

https://pharmahub.org/resources/86/download/ontological_informatics.pdf

Ontological informatics infrastructure for pharmaceutical product development and manufacturing

PHASUITE: AN AUTOMATED HAZOP ANALYSIS TOOL FOR CHEMICAL PROCESSES Part I: Knowledge Engineering Framework

Developing pharmaceutical product formulation in a timely manner and ensuring quality is a complex process that requires a systematic, science-based approach. Information from various categories, including properties of the drug substance and excipients, interactions between materials, unit operations, and equipment is gathered. Knowledge in different forms, including heuristics, decision trees, correlations, and first-principle models is applied. Decisions regarding processing routes, choice of excipients, and equipment sizing are made based on this information and knowledge. In this work, we report on the development of a software infrastructure to assist formulation scientists in managing the information, capturing the knowledge, and providing intelligent decision support for pharmaceutical product formulation.

/pdf/toward-intelligent-decision-support-for-pharmaceutical-sne3wvvz39.pdf

Toward intelligent decision support for pharmaceutical product development

PHASuite: An Automated HAZOP Analysis Tool for Chemical Processes

Process safety analysis is an information intensive task. Collecting all the necessary information from different sources is time consuming and error prone. Most of the information may already be available in some electronic formats. Sharing and reusing this process information can save significant time and effort. Using ontology can greatly enhance the communication and inter-operability among different systems. In this work, ontologies are designed for various kinds of process information and safety analysis results. PHASuite, an intelligent software system for performing automated process safety analysis, has been built based on these ontologies. Appropriate information sharing schemes have been proposed and implemented in PHASuite. It is found, based on several industrial case studies, that information sharing can greatly reduce time and errors associated with reentering information.

Chunhua Zhao

Papers

Ontological informatics infrastructure for pharmaceutical product development and manufacturing

PHASUITE: AN AUTOMATED HAZOP ANALYSIS TOOL FOR CHEMICAL PROCESSES Part I: Knowledge Engineering Framework

Toward intelligent decision support for pharmaceutical product development

PHASuite: An Automated HAZOP Analysis Tool for Chemical Processes

Roles of ontology in automated process safety analysis