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 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 59, 2017 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.aidic.it/cet 

Guest Editors: Zhuo Yang, Junjie Ba, Jing Pan 
Copyright © 2017, AIDIC Servizi S.r.l. 
ISBN 978-88-95608- 49-5; ISSN 2283-9216 

Research on the Design of Ceramic Kiln Control System 

Based on PID 

Jie Zhang 

School of Art Design, Luoyang Normal University, Luoyang 471002, China 

Jie@163.com  

In this paper, the author discussed the application and design of ceramic kiln control system based on PID. 

Ceramic kiln plays a very important role in the process, and the level of automation in kiln determines the 

development of the ceramic industry. The firing of ceramic kiln is a thermo technical process with the variation 

of time, large time delay and much interference. The temperature control system is the core of the whole 

ceramic kiln control system. In this paper, in order to improve a wide range of temperature control accuracy, 

by combining fuzzy and PID control methods a fuzzy PID temperature control system of the thermal analyzer 

based on ARM microprocessor is designed and implemented. In this paper, the hardware has been designed 

by using 32-bit ARM7 processor LPC2368 as the core. The system selected the temperature sensor of 

Platinum-Rhodium thermocouple. The zero-crossing detection opt coupler devices and TRIAC make up the 

implementation unit. Human-computer interaction interface composes of the key and LCD. 

1. Introduction 

Ceramic kiln plays a very important role in the process, and the level of automation in kiln determines the 

development of the ceramic industry. The firing of ceramic kiln is a thermo technical proces s with the 

variation of time, large time delay and much interference (Jing et al., 2008). 

It’s a broad application to measure and control temperature in the industry production. Especially in some 

industry, such as oil, chemistry, electric power, metallurgy. The temperature control directly impacts on the 

quality of the product and industrial production process (Dinh and Afzulpurkar, 2007; Yang et al., 2012). With 

the rapid development of the microelectronics technology, the embedded technology and the automatic 

control theory, the temperature control technology is in the trend of intelligence development. In this paper, on 

the basis of relevant research both at home and abroad, process control for industrial temperature control 

system, and aiming at the feature of bad expand D/A ability, the bad disposal of complex controlled 

environment and the nonsupport of fulfilling the real-time multi-tasking, an industrial fuzzy PID temperature 

control system based on ARM is proposed. In hardware, the system adopt ARM9 embedded micro controller 

as the main control chip, the temperature detection module and LCD module have been designed, and the 

storage unit has been extended. In addition, in order to download D/Ata, system debugging and with the PC 

machine or device to communicate more easily, the system also design to RS232 serial port circuit, JTAG 

circuit and Ethernet interface circuit; In software, the kernel of embedded real-time operating system Linux 

was cut, configured, compiled and transplant in the final. In addition, the programming of applications of 

temperature detection module, D/A D/Ata acquisition module, LCD module and the control algorithm module 

has been design. The temperature control system with this method, overcome the shortage of traditional PLC 

and SCM control, the system has many advantages such as good expand D/A ability, high reliability, fast 

response, small size and more intelligent, etc. In this paper, the algorithm of temperature control in industrial 

production process has been studied, finally, fuzzy PID control was chosen as the control algorithm in this 

temperature control system. To the boiler steam temperature as control object, a simulation model with the 

temperature control system of industrial boiler steam has been established. The result show that the fuzzy PID 

control effectively improve the system capacity, such as nonlinear, time variability and uncertainty, get better 

control effect. 

                               
 
 

 

 
   

                                                  
DOI: 10.3303/CET1759119

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Please cite this article as: Jie Zhang, 2017, Research on the design of ceramic kiln control system based on pid, Chemical Engineering 
Transactions, 59, 709-714  DOI:10.3303/CET1759119   

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2. Overview 

The mathematical model of the controlled process could be established through analysing its mechanism 

according to the relation of material balance and energy balance with mathematical description, this method 

has much universality. The car spray-paint workshop is divided into 2 floors, also its length is 260m and width 

is 50m, building area is about 26000m². The first floor are placed the office and machine equipment’s, on the 

second floor is the ceramic kiln system which is made up of 5 big assembled ceramic kiln equipment’s. The 

big assembled ceramic kiln equipment’s can produce the work demanded air and send it to all over the 

workshop. In normal situation, 5 assembled needs to send 280000 m3/h air (Jin et al., 2008; Zhu and Wan, 

2011; Chen et al., 2009). 

From the Fig. (1), we can see that when the ceramic kiln is working, the two fans are running, outside fresh air 

enters the ceramic kiln system through the “air in gate”, then into the first filtration system which can make air 

clean, then into the heat machine which can heat the air at a certain value, after that the air is filtrated again, 

then it enters spouting water system which can make air wet to reach a certain humidity, finally the air enters 

the workshop by “air out gate”. 

3. Modeling of ceramic kiln control system 

In order to demonstrate the effectiveness of the proposed microcontroller based PID controller a dc-dc 

converter system was chosen for the practical implementation. The phenomenal advancement in technology 

has given rise to modern electronic systems that require reliable, high quality, efficient, small and lightweight 

power supply system. This has inspired the wide use of dc-dc systems in many industrial and electrical 

systems. The main function of a DC-DC converter is to convert a fluctuating DC input voltage into a regulated 

DC output voltage and supply it to a variable-load resistance. DC-DC converters are mostly applied in 

applications such as computers, television receivers, communication devices, medical instrumentation, battery 

chargers and many other devices that require regulated DC power. DC-DC converters are also used for DC 

motor speed control applications to provide regulated variable DC voltage. The dcdc converters can be found 

as step down (buck) or step up (boost) converters. In this project the buck converter system was chosen for 

our analysis and demonstration of the microcontroller based PID controller. In order for a buck converter to 

maintain a constant voltage output, it employs a feedback or closed loop system which continuously monitors 

the output voltage and takes corrective action whenever the output voltage shifts away from the desired value. 

What this corrective action does is to change the duty cycle of the signal driving the MOSFET. Our proposed 

micro-controller based PID controller was going to be that feedback loop which adjusts the duty cycle of the 

MOSFET to maintain a constant output voltage on our converter (Mezquita et al., 2014; Sonnemann and 

Chhay, 2014). 

Because there are many uncertain factors within the room, it's impossible to correctly determine the 

mathematical model of the ceramic kiln room temperature through process identification. The mathematical 

model of the controlled process could be established through analysing its mechanism according to the 

relation of material balance and energy balance with mathematical description, this method has much 

universality. Because the space of the controlled ceramic kiln room is typically large, the change of 

temperature and humidity is unreactive, there is strong anti-interference capability by itself, and therefore we 

decided to establish the mathematical model of the controlled process through mathematical derivation. At the 

same time, we know that the temperature and humidity of the room are determined by many factors, such as 

the temperature and humidity of the outdoor atmosphere, the structure and material of the external wall, the 

orientation of the room, the power of the heating equipment within the room, the quantity of people and the 

working nature of the people, and these factors have their own uncertainties, so it's difficult to get the accurate 

mathematical model of the ceramic kiln room temperature (Krmpotic, 2015). 

The flow characteristic of the regulating valve is the relation between the relative flow of the regulating valve 

and the relative opening of the regulating valve, and its function expression is (Schnepp et al., 2016; Zhu and 

Li, 2014):  

max

( )
Q l

f
Q L

   (1) 

Relative flow Q/Qmax is the ratio of the flow under certain opening Q to the flow under full opening Qmax of the 

control valve. Relative opening l/L is the ratio of the travel under certain opening l to the travel under full 

opening L of the control valve. The electric regulating valve selected in this project has equal percentage 

(logarithm) flow characteristic, the function expression of the regulating valve is: 

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( -1)

max

l

L
Q

R
Q

   (2) 

According to the model of the electric valve we selected, R=30. In this project, the opening of the electric valve 

is controlled by the voltage of 0-10V, the control voltage is expressed as ΔU, so expression (2) is changed to: 

30

30*
)(

10
max

U

Q
tQ



   (3) 

DN25 electric regulating valve is used in this unit, the heating capacity of the valve under full opening is 

150KW, and the vapour volume flowing through the valve is 0.15m3/s, which is substituted into the above 

expression (3), the result is 

1030*004.0)(

U

tQ



   (4) 

In this ceramic kiln system, 0.2Mpa vapour is used as the heat source of the heating air of the heating coil. 

Just like the ordinary heat exchanger, it's considered that the heat dissipated from the heater is approximately 

equal to the heat absorbed by air flowing through the heater, thus we establish the following equation: 

SKP
rCQtQC 2.1   (5) 

Where, Cp is specific heat at constant pressure of air; Cp=1.01KJ/kg*°C; Qk is the air volume flowing through 

the heater m3/s; in this design, the combined ceramic kiln system is constant air volume air supply system, 

Qk=6.5m3/s; r is the latent heat of vaporization of 0.2Mpa vapour, r=2164 kg; C is the specific gravity of 

0.2Mpa vapour, C=1.651kg/m3; Δt is the temperature rise of the air after flowing through the heater, in °C; Qs 

is the vapour flow entered into the heater, in m3/s 

Substitute the above known quantities into the above expression (5), the result is: 

Δt=450Qs, the result after Laplace transform is :  

450
)(

)(




sQ

st

S

  (6) 

The volume of the plant ceramic kiln area is V (m3), the indoor temperature is tn (°C), the outdoor temperature 

is tw (°C), the air supply temperature is ti (°C), the heat supplied into the room by the unit is Qi (kJ/h), the air 

exchange rate of the ceramic kiln room is n(1/h); the heat generated by the machines and persons within the 

plant is Qm (kJ/h); the specific heat at constant volume is Cv ( kJ/ m3*k), the parameter regulated is the indoor 

air temperature tn (°C). 

The heat dissipated from the ceramic kiln room is Qout, the heat taken by the return air is Qout,a, and the heat 

permeated through the enclosure is Qout,b, i.e.:  

boutnvboutaoutout
QVtnCQQQ

,,,
   (7) 

The heat input into the ceramic kiln room Qin is the heat input through air supply Qin,n and the heat generated 

by the machines and persons within the room Qp, 

pivpninin
QVtnCQQQ 

,
  (8) 

The heat storage capacity of the indoor air W is: 

nv
VtCW    (9) 

Assuming that the enclosure and equipment don't store heat, then the equation of heat storage capacity 

change of the ceramic kiln room is 

outin
QQ

dt

dW
   (10) 

Substitute (7), (8), (9) into (10), the result is 

boutnvpiv
n

v
QVtnCQVtnC

dt

dt
VC

,
   (11) 

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For simplifying calculation, assuming the enclosure doesn't transfer heat, ignoring the heat generated by 

persons and equipment, then Qout,b=0, Qp=0 substitute it into (11), then the result is 

ni
n ntnt

dt

dt
   (12) 

After Laplace transform, the equation (12) is: 

1

1

)(

)(





n

sst

st

i

n

  (13) 

In the plant, the volume of the area controlled by one ceramic kiln is 2800m3, the air supply volume of the 

combined ceramic kiln is 50000m3/h and the air exchange rate per second of the room is: 

n=50000/(28003600)=0.00496, substitute it to (13), the result is: 

)(
1202

1
)( st

s
st

in


   (14) 

The distance between the ceramic kiln area and the unit is 50m, which are connected via air duct, the air 

speed of the air supplied by the ceramic kiln is 7m/s, then there is about 7s delay of the room's ceramic kiln, 

therefore the transfer function of the area to be conditioned is 

)(
1202

1
)(

7
ste

s
st

i

s

n






  (15) 

Substitute (4) into (6), then the temperature rise of the unit is:  t=1.830
𝑈

10
, derive  

1030*8.1

U

wi
tt




  

(16) 

For simplifying calculation, assuming the outdoor temperature varies linearly with time tw=13sin((t-6)/12)-7, 

the original point of t is 0. 

4. Algorithm simulation of ceramic kiln system temperature control 

According to the transfer function (15), we established the simulation model as Figure 1. with Simulink of 

Matlab, if disturbance signal is considered, the system will become unstable, traditional PID control cannot 

meet the requirements. In order to stabilize the system, we designed a fuzzy PID adaptive control. 

 

  

Figure 1: (a) Structure diagram of the assembled ceramic kiln and (b) Simulation model 

First, we should choose the fuzzy variables of Kp, Ki, Kd, which are listed in table 1. Then, we choose fuzzy 

variables of E and EC, which are listed in table 2. 

An intelligent adaptive control algorithm is designed in Matlab to see Figure 2. 

 

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Figure 2: PID adaptive control simulation algorithm 

According to the actual running state of the ceramic kiln, we use MATLAB to establish the fuzzy rules in Figure 

3(a). when the ceramic kiln is working, the two fans are running, outside fresh air enters the ceramic kiln 

system through the “air in gate”, then into the first filtration system which can make air clean, then into the heat 

machine which can heat the air at a certain value, after that the air is filtrated again, then it enters spouting 

water system which can make air wet to reach a certain humidity, finally the air enters the workshop by “air out 

gate”. 

 

  
(a) (b) 

Figure 3: (a) PID rules and (b) PID adaptive control simulation char 

The PID adaptive control simulation result chart is in Figure 3 (b). 

Table 1: Fuzzy variables of Kp, Ki, Kd 

Var 0 1 2 3 4 5 6 7 8 9 

Z0 1 0.67 0.33 0 0 0 0 0 0 0 

PS 0 0.33 0.67 1 0.67 0.33 0 0 0 0 

PM 0 0 0 0 0.33 0.67 1 0.67 0.33 0 

PB 0 0 0 0 0 0 0 0.33 0.67 1 

Table 2: Fuzzy variables of E and EC 

Var -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 

NB 1 0.5 0 0 0 0 0 0 0 0 0 0 0 

NS 0 0 0 0.5 1 0.5 0 0 0 0 0 0 0 

Z0 0 0 0 0 0 0.5 1 0.5 0 0 0 0 0 

PS 0 0 0 0 0 0 0 0.5 1 0.5 0 0 0 

PB 0 0 0 0 0 0 0 0 0 0 0 0.5 1 

 

Control engineering is one of the most important part of modern day industries. Its main objective is to avoid 

disturbances and ensure the desired output in industrial processes. One of the generic control strategies that 

is widely used in industrial control is the Proportional Integral and Derivative (PID) control algorithm. The PID 

controller has been deemed by some experts to represent the ultimate in control of continuous processes for 

which a specific mathematical description (transfer function) can be written. The PID controller in all its ability 

to eliminate steady-state errors and anticipate the future through integral action and derivative action is a 

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simple implementation of the feedback principle. More than 95 percent of the control loops in process control 

are of PID type; a greater number of those loops are actually PI control. PID controllers come in many different 

forms. They can be implemented as stand-alone systems in boxes for one or a few loops or as distributed 

systems for process control. Even systems which are as diverse as atomic force microscopes, cars cruise 

control systems, or CD and DVD players contain PID controllers. Because they are sufficient for many control 

problems with benign process dynamics and modest performance requirements, they are found in large 

quantities in almost all industries. 

For many years the PID controllers have been implemented in analog format. After their introduction, these 

electronic controllers gradually excelled in performance over their mechanical predecessors, both in terms of 

performance, speed and cost. As result single loop analog controllers have enjoyed a steady popularity and 

they have reached the highest level of sophistication due to the rapid advances made in the industry. But the 

use of these analog PID controllers however always presented difficulties in changing the controller 

parameters whenever there are changes in plant dynamics due to changes in the operating conditions. This 

scenario always required the engineer to change the hardware in order to change the controller parameters to 

match with the new operating conditions. During that time digital computing had not yet advanced and it was 

very slow and costly compared to the situation today. There was little software available and machine code 

was used to program the required solution to engineering problems. 

5. Conclusions 

In China, when people do actual engineering projects of factory ceramic kiln, due to the influence of input 

temperature change and other disturbance, regular PID control method cannot meet the demand of 

temperature control system. So, in order to solve the temperature control problem, this paper explored the 

model of the temperature control system and established the model of the ceramic kiln.  

Also, we designed the fuzzy PID adaptive algorithm and carried out simulation on the model in Matlab, 

through the simulation result in Fig. (5), it could be seen that the control temperature value could reach the set 

temperature value stably.  

We have applied the algorithm to the actual control system, the actual temperature control system of the 

ceramic kiln has been running for more than 3 years and it is in a good condition. Practice shows that the 

fuzzy adaptive PID controller has been completely met the system’s control demands. Obviously, the ceramic 

kiln model established in this paper is very important and useful to study the temperature control of the 

ceramic kiln. 

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