Tetsuji TANI's research while affiliated with Idemitsu Kosan and other places
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Publications (10)
In transient states of feed oil switching and operation mode changing, the nonlinear response with a time delay makes difficult to control a plant automatically. However, a well experienced operator can control it by his experience in such a state. We propose a hybrid control system by replacing the procedure of a well experienced operator. The pro...
Shown in this paper is a practical method of control using neural
network and fuzzy control techniques, where a neural network estimates
the target of fuzzy control. The neural network is used to estimate the
transient state of a plant which has nonlinear processes such as
refrigerating and filtering. The suitable control target pattern for
fuzzy c...
This paper proposes a practical control method using neural
networks and fuzzy control techniques. Neural networks effectively
simulate a well-experienced operator's procedure to control the tank
level with estimation of a rough control target. Fuzzy control
techniques compensate the estimated rough control target using
operator's knowledge. The co...
Presents a practical method of control using conventional PID and
fuzzy control, where the output of fuzzy reasoning compensates for the
output of PID control directly. This method of control applies to
control of the top temperature (temperature of the stripper top) of a
petrochemical plant. It is shown that the proposed system can control
the top...
A practical control method using neural network and fuzzy control techniques is applied to the level control of the tank in a solvent dewaxing plant. The purposes of the control are to stabilize the tank level and to smoothly change the outflow rate from the tank which conflicts with the stabilization of the tank level.This plant has non-linear cha...
This paper presents a control system for petrochemical plants
using neural networks. Neural networks effectively simulate a
well-experienced operator's procedure to control the tank level of the
plant even in a transient state such as feed oil switching. This control
system is applied to tank level control of a desulfurizing plant during
feed oil s...
The authors propose a practical control method using neural
networks and fuzzy control techniques, whereby neural networks estimate
the target of fuzzy control. Neural networks estimate the transient
state of a plant which has a nonlinear process such as refrigeration and
filtering. Based on the estimation, a suitable control target pattern
for fuz...
Knowledge acquisition is a critical stage in developing expert systems. ID3 is an approach to overcome it.ID3 can create crisp IF-THEN rules automatically based on entropy of cases. However the number of rules increases when there are many cases even if they have slightly different values.Fuzzy modeling technique is applied to many fields because i...
The authors propose a practical method for fuzzy modeling. The ID3
algorithm, used in the field of machine learning, was applied to select
the effective variables in the premises of a fuzzy model and compute
their boundary values. Even when the process had many variables,
effective variables were chosen and their boundary values for
fuzzification w...
PID control has been widely used in process control. The main feature of PID control is a simple algorithm constructed by P-action, I-action and D-action.It is, however, difficult to satisfy control performance commonly for different conditions such as start-up, shut-down and feed switching. In many such process control systems, expert operators pl...
Citations
... In that sense, two industrial fields where the control systems have been widely adopted and developed over the last years are the petrochemical industries and the wastewater facilities. Proportional Integral (PI) and Proportional Integral Derivative (PID) [2] controllers have been considered in [3][4][5]. In [3], two PI controllers have been proposed in a Wastewater Treatment Plant (WWTP) to control the dissolved oxygen in the fifth reactor tank (S O,5 ) and the nitrite-nitrogen in the second one (S NO,2 ). ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/543741539819521-1506649493073_Q64/Kazuo-Tanaka.jpg)
... The application of a neural network trained for a given set of inputs and output was published in 1993 [2] which was used for level control in petrochemical tanks. ...
... [46], Kim ve ark. [47], Zhou [48] Parametre Belirleme Chiang ve Su [49] Kalite Tahmini Smith ve Yazıcı [45], Tani ve ark. [50], Liu [51], Chang ve Jiang [38] , Huang [33], Paiva ve ark. [52] Model Formüle Etme Park ve ark. ...
... The main idea behind this approach, is that the Random Forests (RF) will detect the boundary of critical regions in the feature space that are important for predicting the outcome variable in an efficient way. This similar to the approach used in [18], [19], [20] where boundaries obtained from a crisp decision tree learned by CART or ID3 are fuzzified in order to derive fuzzy rules. In [18] a crisp decision tree learned by CART is used to set up an initial structure of a Fuzzy Inference System (FIS). ...
... Especially for the latter, minimization of the change in the outflow is highly desired, since the incoming surge should be distributed further with reduced amplitude. In recent years, not only PI(D) controllers were designed for level control of tanks, but also fuzzy control approaches ( Tani et al., 1996;Petrov et al., 2002), as well as optimal averaging strategies (McDonald et al., 1986;Campo and Morari, 1989;Rosander et al., 2012). ...
