An expert system based intelligent control scheme is being developed for the effective control and full automation of bioreactor systems in space. The scheme developed will have the capability to capture information from various resources including heuristic information from process researchers and operators. The knowledge base of the expert system should contain enough expertise to perform on-line system identification and thus be able to adapt the controllers accordingly with minimal human supervision

San, Ka-Yiu

English

NASA Technical Reports Server

An Expert System Based Intelligent 
Control Scheme for Space Bioreactors 
Ka-Yiu San 
Chemical Engineering Department 
Rice University, Houston, Texas 
Abstract 
An expert system based intelligent control scheme is being developed for the 
effective control and full automation of bioreactor systems in space. The scheme developed 
will have the capability to capture information from various resources including heuristic 
information from process researchers and operators. The knowledge base of the expert 
system should contain enough expertise to perform on-line system identification and thus 
be able to adapt the controllers accordingly with minimal human supervision. 
Introduction and Approach 
Mammalian tissue cells are very sensitive to small changes in their growth 
environment (pH, temperature, nutrient and byproduct concentrations) and thus require 
precise regulation on reactor parameters. However, the design and implementation of 
meaningful control strategies are often hindered by the lack of detailed knowledge about 
the system dynamics (growth kinetics of the tissue cells as  an example) and the lack of 
availability of proper sensors. It is further complicated by the possibility of variations in 
process characteristics and parameters during the course of operation, such as change in 
the yield factor of the cells, the number of viable cells and their metabolic activities, etc. 
Under such situation, the proper tuning of a conventional proportional-integral-derivative 
controller can become an insurmountable task. This will, in most cases, result in poor 
reactor performance and possibly may lead to reactor failure. The above scenario clearly 
dictates the need for the development of a more sophisticated control scheme which is 
capable : 1) of providing proper control action despite changes in system dynamics, 2) of 
synthesizing meaningful and effective control strategies under highly uncertain 
environment, and 3) of integrating heuristic information into existing control algorithms to 
improve controller performance. 
A novel controller design approach is being developed based on the observation 
that significant improvement in process performance can be achieved by implementing 
available ad hoc procedures. Most of these ad hoc procedures are in the form of rules that  
are the results of accumulated experiences and knowledge of the researchers or operators. 
These rules of thumb are frequently known as heuristics. Unfortunately, existing 
controller design approaches do not allow easy integration of this type of knowledge 
representation. We are presently developing an  expert system which, in our believe, are 
best suited for the capturing and the utilization of this knowledge to improve controller 
performance. Expert systems are applications of the new and evolving techniques of 
Artificial Intelligence. It is a computer program that contains factual, as  well as 
experimental, intuitive, uncertain, and judgemental knowledge. With this knowledge, the 
program reasons and infers new information to solve problems that are not amenable to 
algorithmic solutions. In general, an  expert system contains three components: a 
knowledge base, a reasoning mechanism and a control mechanism. 
The source of information that are being incorporated into the knowledge base of 
our expert system includes: 1) existing mathematical process models, 2) operators' and 
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https://ntrs.nasa.gov/search.jsp?R=19890010914 2020-03-20T03:56:27+00:00Z
researchers’ experiences, 3) experimental data-base, and 4) up-to-date knowledge in cell 
biology and biochemistry of the process including metabolic pathways, proven or 
speculative kinetic mechanisms and so on (see figure attached). Furthermore, this 
knowledge base will be updated by the incoming process measurements. I t  is through the 
use of this powerful knowledge base that a meaningful control strategy will be synthesized 
and implemented. 
In summary, the expert system developed herein will contain enough knowledge 
and expertise that it is capable of performing the tasks of system identification and process 
controller design with minimal human supervision. In addition, the final version of this 
control scheme will be a highly intelligent machine which is capable of learning by 
extracting information from the incoming measurements of the process variables and thus 
is capable of updating its reasoning pattern in response to changes in process behavior. 
This is the type of bioreactor control which has the best probability of success for long- 
term space bioprocessing applications. 
Results and Discussion 
A study aiming at the investigation of the impact of the addition of a n  expert 
system to a conventional controller has been performed. Of particular interest is the 
feasibility of integrating ad hoc heuristic information to the overall control scheme. The 
control of pH value in a neutralization tank with inlets of different buffering capacities is 
chosen as a sample system. The set-up of this system is schematically shown in Fig. 1. 
The reactor pH is controlled by a PID controller which adjusts the flow rate of acid into the 
tank. Disturbances are introduced by changing the inlet composition according to table 1. 
The closed loop responses of the reactor pH with two fixed gain controllers tuned for 
different buffer content and an expert system consulting controller are shown in Fig. 2. 
The knowledge base of the expert system contains the necessary heuristic information 
about the process. Table 3 shows the square of the derivations of reactor pH from its set- 
point for each of the period as well as the total sum. Result from this study can be 
summarized as follows: 1) In the presence of system dynamic variation, a fixed gain 
controller may be either too tightly or  too loosely tuned which results either in controller 
instability (middle curve of Fig. 2) or sluggish response (middle part of upper curve), and 2) 
An expert system based controller is capable of predicting the reactor buffer content and 
thus is able to select the correct controller setting. This results in a far better controller 
performance. 
In conclusion, results from our studies demonstrate the feasibility as well as the 
potential of using an expert system to capture heuristic information to improve controller 
performance. We believe that this approach is capable to provide a fully automated 
environment with minimal human supervision which is critical for long-term space 
bioprocessing applications. 
The plan for future work in this area is 1) to finish the development of such a n  
expert system, 2) to perform a series of experiments to generate the required knowledge 
base as well as experiences about the process. 
References and Acknowledgement 
1. 
2. 
Cardello R. and K.-Y. San, Proceedings of 1987 American Control Conference, June 
10-12, Minnesota, to appear. 
Cardello R. and K.-Y. San, submitted to Biotechnol. Bioeng., (1987) 
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An expert system based intelligent control scheme for space bioreactors

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