The CIFM places high priority on research. Our current projects fall under one of four development areas for focused research:

 1. Smart sensors and field devices

Field devices are the eyes, ears and hands of process automation. Within the last 50 years, field devices have evolved from pneumatics to electronics. Digital electronic components are now providing field devices with the intelligence to collect information and build knowledge about the process in addition to providing a measurement or control signal to the control system. Our activity in this area is focused on creating sensors that provide information regarding the food quality, food safety and to anticipate process failures.

2. Process Modeling

We are actively involved in developing first-principles as well as semi-empirical models of food processes. First principle models are ideal for process design wheras semi-empirical models are more suited for online control. Both types of modeling are necessary Where applicable, these models incorporate fundamental knowledge of food chemistry and microbiology to provide a truly custom solutions to difficult food processing problems.

3. Intelligent model-based control strategies

Models of how food behaves during processing can be used as a foundation for control system design and algorithm development. Model-based predictive control can decrease variability in finished product quality as feed and material disturbances affect the raw material and utilities.
 
A food processing plant is formed from the integration of multiple unit operations and result in a complex, non-linear system. Disturbances to one unit operation ripple throughout the system network. Often it is unclear how to manage multiple operations when one operation is disabled. The common methods of handling production crisis are based on experience and expert systems. However these solutions don't work for new scenarios that have never been previously experienced. Non-linear network models aid the decision making process in controlling entire plant operations in the the event of plant failures.

4. Knowledge technology

Process data must be converted into information. Process information must be converted into knowledge. The automation of this process is called knowledge technology.
 
Fundamental questions regarding this area include:

• How much data is necessary to acquire knowledge required to solve a particular manufacturing problem?
• How much raw process data should be retained for historical purposes?
• How should one implement a safe and robust electronic record keeping system?
• How should a control system be validated?
• How can computers be used to better train students and personnel?
• How can the Human Machine Interface be better engineered?