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The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively.

The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively.

The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively.

The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively.

The aim of this research is to control the reactor temperature of an exothermic batch process. During the process, large amount of heat will be released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. In practice, heat is needed to speed up the reaction rate so that the overall process cycle time can be reduced whereas the cooling is employed to cool down the reactor in order to reduce excessive heat. Hence, this paper proposes genetic algorithm (GA) to control the process temperature with a predetermined temperature profile. GA exploits the probabilistic search method to optimise the specific objective function based on the evolutionary principle. Simulation assessment of the GA has been carried out using a benchmark exothermic batch process model. The results show that GA is able to control the reactor temperature effectively.

The application of solar energy conversion has been extensively utilized as an alternative energy source to generate heat. This approach would be a step towards sustainable energy development particularly in the manufacturing industry with energy-intensive process. In this paper, thermal enhancement on the key component of a solar energy device – solar heat collector (SHC), has been evaluated by proposing a baffle-type SHC with various geometric configuration in the air passage namely longitudinal baffle and transversal baffle. The performance of SHC is evaluated in term of efficiency, temperature distribution, airflow pattern and pressure drop across the collector outlet through Computational Fluid Dynamic (CFD) investigation. It was observed that maximum collector efficiency was achieved in the Longitudinal-SHC (L-SHC), with a value of 46.2 % followed by Transversal-SHC (T-SHC) and without baffles. Maximum drying temperature at the collector outlet was 332.43 K for L-SHC, showing temperature rise of 0.35 % and 4.21 % from T-SHC and without baffles, respectively. The velocity vector indicated that turbulence flow was created in the T-SHC which consequently improved the heat transfer. Whereas in L-SHC, enhancement was achieved through the prolonged heating time in the passage. Considering the thermo-hydraulic performance factor evaluated, these enhancement features had diminished the effect of pressure drop.

Single-storey terraced housing in Malaysia does not provide thermal comfort to its occupants due to poor dissipation of heat from solar irradiation unless mechanical cooling is installed, which adds to the urban heating island problem. This study is a simple assessment of natural ventilation performance of terrace houses which by law are built with an air well and sized according to regulations. A typical room of 3 m x 4 m x 3 m with an adjacent air well has been investigated by CFD simulation assuming ambient temperature of 30℃ and atmospheric pressure of 101.3 kPa. The results show that with an air well-chimney of 2.55 m above the roof the test room air velocity could reach 0.6 ms˗¹ for thermal comfort; while without the protruded chimney adverse cold inflow set in, and the indoor air flow velocity ranged from as low as 0.08 ms-1 to 0.21 ms ˗¹. However, when the protruded chimney was not installed, but with a wire mesh-based blocker of adverse cold inflow installed on the air well outlet, the indoor air flow velocity consistently reached 0.2-0.3 ms˗¹, which was about 45% higher than that without the blocker, and was within the recommended range for thermal comfort. The wire mesh blocker of adverse cold inflow could be an option to ventilate effectively single-story terrace houses without the need of installing tall chimneys.

This paper proposes genetic algorithm (GA) to optimize the productivity of yeast fermentation process. The proposed optimizer aims to maximize yeast productivity while minimizing the by-product of ethanol. Various initial glucose concentrations will affect yeast productivity and influence the performance of yeast fermentation. Yeast has relatively high ethanol production as compared with other microorganisms. Since the excessive ethanol formation in the yeast fermentation process will have a negative impact on quality of the product, it is needed to optimize glucose feeding rate at optimal level for maximizing the yeast productivity. The conventional open-loop feeding system is inadequate to minimize the growth of by-product as the system will not regulate the glucose feeding rate based on the instant needs. Thus, GA is proposed to optimize the glucose feeding profile based on the instant concentration of yeast, glucose, oxygen and ethanol inside the fermentation tank. The results show the proposed GA can obtain a higher yield production of 95.3% as compared to the conventional open-loop system with 92.5% yield production. The results reveal that the optimal glucose feeding rate using GA is achieved satisfyingly and successfully.

This paper proposes genetic algorithm (GA) to optimize the productivity of yeast fermentation process. The proposed optimizer aims to maximize yeast productivity while minimizing the by-product of ethanol. Various initial glucose concentrations will affect yeast productivity and influence the performance of yeast fermentation. Yeast has relatively high ethanol production as compared with other microorganisms. Since the excessive ethanol formation in the yeast fermentation process will have a negative impact on quality of the product, it is needed to optimize glucose feeding rate at optimal level for maximizing the yeast productivity. The conventional open-loop feeding system is inadequate to minimize the growth of by-product as the system will not regulate the glucose feeding rate based on the instant needs. Thus, GA is proposed to optimize the glucose feeding profile based on the instant concentration of yeast, glucose, oxygen and ethanol inside the fermentation tank. The results show the proposed GA can obtain a higher yield production of 95.3% as compared to the conventional open-loop system with 92.5% yield production. The results reveal that the optimal glucose feeding rate using GA is achieved satisfyingly and successfully.

This paper proposes genetic algorithm (GA) to optimize the productivity of yeast fermentation process. The proposed optimizer aims to maximize yeast productivity while minimizing the by-product of ethanol. Various initial glucose concentrations will affect yeast productivity and influence the performance of yeast fermentation. Yeast has relatively high ethanol production as compared with other microorganisms. Since the excessive ethanol formation in the yeast fermentation process will have a negative impact on quality of the product, it is needed to optimize glucose feeding rate at optimal level for maximizing the yeast productivity. The conventional open-loop feeding system is inadequate to minimize the growth of by-product as the system will not regulate the glucose feeding rate based on the instant needs. Thus, GA is proposed to optimize the glucose feeding profile based on the instant concentration of yeast, glucose, oxygen and ethanol inside the fermentation tank. The results show the proposed GA can obtain a higher yield production of 95.3% as compared to the conventional open-loop system with 92.5% yield production. The results reveal that the optimal glucose feeding rate using GA is achieved satisfyingly and successfully.

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.

Thermal runaway of exothermic reaction, especially with batch chemical plants, is a critical issue because it can affect production quantity and quality, causes profit loss, and in worst case can threaten the safety of workers and lead to plant accident. The conventional PID controller which is usually used in many industries needs to be operated under supervision in order to perform the temperature control well. In this work, Mamdani type fuzzy logic controller is proposed to track an optimal reference temperature profile. Both process model and proposed controller are simulated using MATLAB. The results show the designed fuzzy logic controller is well-performed in set point tracking and able to cope with the simulated disturbance condition.

This paper presents a metaheuristic-based temperature controller for an exothermic batch process. Developing a suitable temperature controller for an exothermic process is a challenging task because large amount of heat is released rapidly during the process. The exothermic behavior will further increase the reaction rate and cause more heat to be liberated. As the result, the improper temperature control might cause the reaction becomes unstable and consequently poses safety concern to the plant personnel and equipment. The conventional non-metaheuristic-based controller, such as fuzzy logic requires empirical data or knowledge about the total amount heat released while developing its fuzzy rules and membership functions for precision control. However, the detailed kinetic model of the heat released is unable to be obtained since there are several unobservable parameters during the process, such as the energy held in the reactor and jacket walls. Therefore, particle swarm optimization algorithm (PSO) is proposed as the controller to maintain the reactor temperature at the desired trajectory by manipulating the inlet jacket fluid temperature and flow rate. The simulation results show the proposed PSO produces better performances in terms of minimizing fluctuation in control actions and overshooting as compared with the conventional fuzzy logic controller.

This paper presents a metaheuristic-based temperature controller for an exothermic batch process. Developing a suitable temperature controller for an exothermic process is a challenging task because large amount of heat is released rapidly during the process. The exothermic behavior will further increase the reaction rate and cause more heat to be liberated. As the result, the improper temperature control might cause the reaction becomes unstable and consequently poses safety concern to the plant personnel and equipment. The conventional non-metaheuristic-based controller, such as fuzzy logic requires empirical data or knowledge about the total amount heat released while developing its fuzzy rules and membership functions for precision control. However, the detailed kinetic model of the heat released is unable to be obtained since there are several unobservable parameters during the process, such as the energy held in the reactor and jacket walls. Therefore, particle swarm optimization algorithm (PSO) is proposed as the controller to maintain the reactor temperature at the desired trajectory by manipulating the inlet jacket fluid temperature and flow rate. The simulation results show the proposed PSO produces better performances in terms of minimizing fluctuation in control actions and overshooting as compared with the conventional fuzzy logic controller.

This paper presents a metaheuristic-based temperature controller for an exothermic batch process. Developing a suitable temperature controller for an exothermic process is a challenging task because large amount of heat is released rapidly during the process. The exothermic behavior will further increase the reaction rate and cause more heat to be liberated. As the result, the improper temperature control might cause the reaction becomes unstable and consequently poses safety concern to the plant personnel and equipment. The conventional non-metaheuristic-based controller, such as fuzzy logic requires empirical data or knowledge about the total amount heat released while developing its fuzzy rules and membership functions for precision control. However, the detailed kinetic model of the heat released is unable to be obtained since there are several unobservable parameters during the process, such as the energy held in the reactor and jacket walls. Therefore, particle swarm optimization algorithm (PSO) is proposed as the controller to maintain the reactor temperature at the desired trajectory by manipulating the inlet jacket fluid temperature and flow rate. The simulation results show the proposed PSO produces better performances in terms of minimizing fluctuation in control actions and overshooting as compared with the conventional fuzzy logic controller.

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

Sensory evaluation plays an important role in the quality control of food productions. Sensory data obtained through sensory evaluation are generally subjective, vague and uncertain. Classically, factorial multivariate methods such as Principle Component Analysis (PCA), Partial Least Square (PLS) method, Multiple Regression (MLR) method and Response Surface Method (RSM) are the common tools used to analyse sensory data. These methods can model some of the sensory data but may not be robust enough to analyse nonlinear data. In these situations, intelligent modelling techniques such as Fuzzy Logic and Artificial neural network (ANNs) emerged to solve the vagueness and uncertainty of sensory data. This paper outlines literature of intelligent sensory modelling on sensory data analysis.

The red alga Kappaphycus alvarezii has been reported to be a potential raw material for functional food due to its high content of soluble dietary fibre, mineral, omega-3 fatty acids as well as a substantial amount of essential amino acids. In order to benefit from these excellent nutritional properties, this project aimed to develop a high-value dehydrated snack from K. alvarezii using osmotic dehydration (OD) treatment prior to hot air-drying. A 3 × 3 factorial design with 50°, 60° and 70°Brix sucrose concentration as well as treatment temperatures of 30, 35 and 40 °C were used. In general, an increase in sucrose concentration and temperature promoted mass transfer. OD treatment using 70°Brix sucrose concentration at 40 °C caused case hardening of the seaweed that reduced the solid gain (p  0.05), but dehydrated seaweed became darker at high sugar concentration. Interaction effect between sucrose concentration and temperature was found to affect the water loss and solid gain of the OD treatment (p 

The aim of this chapter is to optimise the productivity of an exothermic batch process, by maximising the production of the desired product while minimising the undesired by-product. During the process, heat is liberated when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety issues. In the industries, a dual-mode controller is used to control the process temperature according to a predetermined optimal reference temperature profile. However, the predetermined optimal reference profile is not able to limit the production of the undesired by-product. Hence, this work proposed a genetic-algorithm-based controller to optimise the batch productivity without referring to any optimal reference profile. From the simulation results, the proposed algorithm is able to improve the production of the desired product and reduce the production of the undesired by-product by 15.3 and 34.4 %, respectively. As a conclusion, the genetic-algorithm-based optimisation performs better in raw materials utilisation as compared to the predetermined optimal temperature profile method.

Industrial fed-batch yeast fermentation process is a typical nonlinear dynamic process that requires good control technique and monitoring to optimize the yeast production. This chapter explores the applicability of Q-learning in determining the feed flow rate in a fed-batch yeast fermentation process to achieve multiobjectives optimization. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning is well known for its interactive properties with the process environment and is suitable for the learning of system dynamic. Therefore, the utilization and performance of Q-learning to seek for the optimal gain for the controller is studied in this chapter. Meanwhile, the performance of Q-learning under the process disturbance is also tested. © Springer Science+Business Media New York 2013.

The aim of this chapter is to optimise the productivity of an exothermic batch process, by maximising the production of the desired product while minimising the undesired by-product. During the process, heat is liberated when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety issues. In the industries, a dual-mode controller is used to control the process temperature according to a predetermined optimal reference temperature profile. However, the predetermined optimal reference profile is not able to limit the production of the undesired by-product. Hence, this work proposed a genetic-algorithm-based controller to optimise the batch productivity without referring to any optimal reference profile. From the simulation results, the proposed algorithm is able to improve the production of the desired product and reduce the production of the undesired by-product by 15.3 and 34.4 %, respectively. As a conclusion, the genetic-algorithm-based optimisation performs better in raw materials utilisation as compared to the predetermined optimal temperature profile method.

Cold air inflow has significant effect on a natural draft air cooled heat exchanger (NDACHE) chimney. To study the mechanism for the prevention of cold inflow into NDACHE chimney's, in total seven configuration of experiments were carried out In the prototype models for different heat loads at approximately 0.7SkW to 1.25kW. The effect of wire mesh screen on the chimney performance had also determined. Temperature and air velocity were measured for the configurations and heat loads. It was found that the top only, Top and Middle and Middle only installations were able to maintain draft velocity to within 3.0% of , the control experiment draft velocity in spite of the resistance of the mesh. It was also found that configurations utilizing mesh at the bottom had experienced a drop is draft velocity by up to 50% from the control. The smoke flow visualization tests clearly proved that cold air inflow phenomenon exits and can be countered with the installation of wire mesh at the top of the model. In the second phase of the project, the wire mesh of 3 different apertures and two chimney height were tested. Large aperture tended to allow more cold inflow and draft was directly proportional to chimney height. CFD runs were also done for the prototypes to explain the cause of wire mesh serving as repellent of cold inflow.

Industrial fed-batch yeast fermentation process is a typical nonlinear dynamic process that requires good control technique and monitoring to optimize the yeast production. This chapter explores the applicability of Q-learning in determining the feed flow rate in a fed-batch yeast fermentation process to achieve multiobjectives optimization. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning is well known for its interactive properties with the process environment and is suitable for the learning of system dynamic. Therefore, the utilization and performance of Q-learning to seek for the optimal gain for the controller is studied in this chapter. Meanwhile, the performance of Q-learning under the process disturbance is also tested. © Springer Science+Business Media New York 2013.

Cold air inflow has significant effect on a natural draft air cooled heat exchanger (NDACHE) chimney. To study the mechanism for the prevention of cold inflow into NDACHE chimney's, in total seven configuration of experiments were carried out In the prototype models for different heat loads at approximately 0.7SkW to 1.25kW. The effect of wire mesh screen on the chimney performance had also determined. Temperature and air velocity were measured for the configurations and heat loads. It was found that the top only, Top and Middle and Middle only installations were able to maintain draft velocity to within 3.0% of , the control experiment draft velocity in spite of the resistance of the mesh. It was also found that configurations utilizing mesh at the bottom had experienced a drop is draft velocity by up to 50% from the control. The smoke flow visualization tests clearly proved that cold air inflow phenomenon exits and can be countered with the installation of wire mesh at the top of the model. In the second phase of the project, the wire mesh of 3 different apertures and two chimney height were tested. Large aperture tended to allow more cold inflow and draft was directly proportional to chimney height. CFD runs were also done for the prototypes to explain the cause of wire mesh serving as repellent of cold inflow.

Industrial fed-batch yeast fermentation process is a typical nonlinear dynamic process that requires good control technique and monitoring to optimize the yeast production. This chapter explores the applicability of Q-learning in determining the feed flow rate in a fed-batch yeast fermentation process to achieve multiobjectives optimization. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning is well known for its interactive properties with the process environment and is suitable for the learning of system dynamic. Therefore, the utilization and performance of Q-learning to seek for the optimal gain for the controller is studied in this chapter. Meanwhile, the performance of Q-learning under the process disturbance is also tested. © Springer Science+Business Media New York 2013.

Cold air inflow has significant effect on a natural draft air cooled heat exchanger (NDACHE) chimney. To study the mechanism for the prevention of cold inflow into NDACHE chimney's, in total seven configuration of experiments were carried out In the prototype models for different heat loads at approximately 0.7SkW to 1.25kW. The effect of wire mesh screen on the chimney performance had also determined. Temperature and air velocity were measured for the configurations and heat loads. It was found that the top only, Top and Middle and Middle only installations were able to maintain draft velocity to within 3.0% of , the control experiment draft velocity in spite of the resistance of the mesh. It was also found that configurations utilizing mesh at the bottom had experienced a drop is draft velocity by up to 50% from the control. The smoke flow visualization tests clearly proved that cold air inflow phenomenon exits and can be countered with the installation of wire mesh at the top of the model. In the second phase of the project, the wire mesh of 3 different apertures and two chimney height were tested. Large aperture tended to allow more cold inflow and draft was directly proportional to chimney height. CFD runs were also done for the prototypes to explain the cause of wire mesh serving as repellent of cold inflow.

Cold air inflow has significant effect on a natural draft air cooled heat exchanger (NDACHE) chimney. To study the mechanism for the prevention of cold inflow into NDACHE chimney's, in total seven configuration of experiments were carried out In the prototype models for different heat loads at approximately 0.7SkW to 1.25kW. The effect of wire mesh screen on the chimney performance had also determined. Temperature and air velocity were measured for the configurations and heat loads. It was found that the top only, Top and Middle and Middle only installations were able to maintain draft velocity to within 3.0% of , the control experiment draft velocity in spite of the resistance of the mesh. It was also found that configurations utilizing mesh at the bottom had experienced a drop is draft velocity by up to 50% from the control. The smoke flow visualization tests clearly proved that cold air inflow phenomenon exits and can be countered with the installation of wire mesh at the top of the model. In the second phase of the project, the wire mesh of 3 different apertures and two chimney height were tested. Large aperture tended to allow more cold inflow and draft was directly proportional to chimney height. CFD runs were also done for the prototypes to explain the cause of wire mesh serving as repellent of cold inflow.

Industrial fed-batch yeast fermentation process is a typical nonlinear dynamic process that requires good control technique and monitoring to optimize the yeast production. This chapter explores the applicability of Q-learning in determining the feed flow rate in a fed-batch yeast fermentation process to achieve multiobjectives optimization. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning is well known for its interactive properties with the process environment and is suitable for the learning of system dynamic. Therefore, the utilization and performance of Q-learning to seek for the optimal gain for the controller is studied in this chapter. Meanwhile, the performance of Q-learning under the process disturbance is also tested. © Springer Science+Business Media New York 2013.

The aim of this chapter is to optimise the productivity of an exothermic batch process, by maximising the production of the desired product while minimising the undesired by-product. During the process, heat is liberated when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety issues. In the industries, a dual-mode controller is used to control the process temperature according to a predetermined optimal reference temperature profile. However, the predetermined optimal reference profile is not able to limit the production of the undesired by-product. Hence, this work proposed a genetic-algorithm-based controller to optimise the batch productivity without referring to any optimal reference profile. From the simulation results, the proposed algorithm is able to improve the production of the desired product and reduce the production of the undesired by-product by 15.3 and 34.4 %, respectively. As a conclusion, the genetic-algorithm-based optimisation performs better in raw materials utilisation as compared to the predetermined optimal temperature profile method.

The aim of this chapter is to optimise the productivity of an exothermic batch process, by maximising the production of the desired product while minimising the undesired by-product. During the process, heat is liberated when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety issues. In the industries, a dual-mode controller is used to control the process temperature according to a predetermined optimal reference temperature profile. However, the predetermined optimal reference profile is not able to limit the production of the undesired by-product. Hence, this work proposed a genetic-algorithm-based controller to optimise the batch productivity without referring to any optimal reference profile. From the simulation results, the proposed algorithm is able to improve the production of the desired product and reduce the production of the undesired by-product by 15.3 and 34.4 %, respectively. As a conclusion, the genetic-algorithm-based optimisation performs better in raw materials utilisation as compared to the predetermined optimal temperature profile method.

This paper aims optimise the exothermic batch productivity while minimise the waste production by manipulating the fluid temperature and fluid flow rate. During the process, a large amount of heat is released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. Commonly, the optimisation of the batch process is based on the predetermined optimal reference temperature profile. However, this reference profile is unable to limit the waste production effectively. Therefore, multivariable genetic algorithm (MGA) is proposed in this work to optimise the productivity of the process without referring to the predetermined reference profile. The results show that the MGA is able to harvest more than 80 % of yield in handling human error and equipment failure.

This paper aims optimise the exothermic batch productivity while minimise the waste production by manipulating the fluid temperature and fluid flow rate. During the process, a large amount of heat is released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. Commonly, the optimisation of the batch process is based on the predetermined optimal reference temperature profile. However, this reference profile is unable to limit the waste production effectively. Therefore, multivariable genetic algorithm (MGA) is proposed in this work to optimise the productivity of the process without referring to the predetermined reference profile. The results show that the MGA is able to harvest more than 80 % of yield in handling human error and equipment failure.

This paper aims optimise the exothermic batch productivity while minimise the waste production by manipulating the fluid temperature and fluid flow rate. During the process, a large amount of heat is released rapidly when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently poses safety concern to the plant personnel. Commonly, the optimisation of the batch process is based on the predetermined optimal reference temperature profile. However, this reference profile is unable to limit the waste production effectively. Therefore, multivariable genetic algorithm (MGA) is proposed in this work to optimise the productivity of the process without referring to the predetermined reference profile. The results show that the MGA is able to harvest more than 80 % of yield in handling human error and equipment failure.

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition.

Exothermic batch reactions are highly nonlinear and complex processes. During the mixing of the chemical reactants, a sudden and unpredictable heat is released causing exothermic reaction. As a result, the runaway reaction may affect the product quality and pose safety problem to the system. Therefore, it is crucial to control the reactor temperature during the process. Often, good controller requires a precise mathematical model in order to achieve accurate control. However, a precise process kinetic model is not easy to obtain since the process dynamic is changing rapidly. Hence, this paper aims to propose Q-learning (QL) algorithm to observe the process behaviour and to estimate the process parameters through the learning ability. The estimated process parameters are then sent to the genetic algorithm (GA) in order to tune a PID controller through evolutionary process. The results show that the proposed method produces satisfactory results in controlling the reactor temperature

Exothermic batch reactions are highly nonlinear and complex processes. During the mixing of the chemical reactants, a sudden and unpredictable heat is released causing exothermic reaction. As a result, the runaway reaction may affect the product quality and pose safety problem to the system. Therefore, it is crucial to control the reactor temperature during the process. Often, good controller requires a precise mathematical model in order to achieve accurate control. However, a precise process kinetic model is not easy to obtain since the process dynamic is changing rapidly. Hence, this paper aims to propose Q-learning (QL) algorithm to observe the process behaviour and to estimate the process parameters through the learning ability. The estimated process parameters are then sent to the genetic algorithm (GA) in order to tune a PID controller through evolutionary process. The results show that the proposed method produces satisfactory results in controlling the reactor temperature

Exothermic batch reactions are highly nonlinear and complex processes. During the mixing of the chemical reactants, a sudden and unpredictable heat is released causing exothermic reaction. As a result, the runaway reaction may affect the product quality and pose safety problem to the system. Therefore, it is crucial to control the reactor temperature during the process. Often, good controller requires a precise mathematical model in order to achieve accurate control. However, a precise process kinetic model is not easy to obtain since the process dynamic is changing rapidly. Hence, this paper aims to propose Q-learning (QL) algorithm to observe the process behaviour and to estimate the process parameters through the learning ability. The estimated process parameters are then sent to the genetic algorithm (GA) in order to tune a PID controller through evolutionary process. The results show that the proposed method produces satisfactory results in controlling the reactor temperature

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimizing the production of undesired by-product. During the process, a large amount of heat is released rapidly when the reactants are mixed together due to exothermic behavior. This causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. This paper proposes genetic algorithm (GA) as an approach to control the process temperature by changing the coolant temperature because GA does not require the exact process dynamics in advance, which normally in practical, the process dynamics are poorly known in practical. GA is able to evolve itself to obtain an optimum solution to change the coolant temperature. The simulation studies show that the GA will be a good candidate to optimize the process and minimize the temperature overshoot throughout the reaction process. Furthermore, GA is able to evolve itself to obtain an optimum solution to change the coolant temperature.

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition.

The primary aim in batch process is to enhance the process operation in order to achieve high quality and purity product while minimising the production of undesired by-product. However, due to the difficulties to perform online measurement, batch process supervision is based on the direct measurable quantities, such as temperature. During the process, a large amount of exothermic heat is released when the reactants are mixed together. The exothermic behaviour causes the reaction to become unstable and consequently the quality and purity of the final product will be affected. Therefore, it is important to have a control scheme which is able to balance the needs of process safety with the product quality and purity. Since the chemical industries are still applying PI and PID to control the batch process, researchers are keen to optimize PID parameters using artificial intelligence (AI) techniques. However, most of these PID optimization techniques need online process model to predetermine the optimizer parameters. However in practice, the dynamic model of the batch process is poorly known. As a result, majority of the studies focused on acceptable performance instead of optimum performance of the batch process control. This paper proposes a new genetic algorithm (GA) optimizer which consists of additional information of the online estimated model parameters in addition to the PID parameters as the string of the GA. The simulation results show that the proposed GA auto-tuning method is a better candidate than the regular GA where the estimated model parameters in fitness function is capable to control the process temperature while avoiding model mismatch and disturbance condition.

Industrial fed-batch yeast fermentation process is a typical highly nonlinear dynamic process that requires good controlling technique and monitoring to achieve optimization. Due to the various uncertainties in the process, optimization to get the desired product quality with less capital and without compromising the environment is therefore, and has always been an issue of interest in chemical process control. This research aims to report an optimization in substrate feeding to maximize the yeast production and to minimize the production of ethanol under the influence of process disturbance using Q-learning (QL) algorithm. QL is a form of reinforcement learning. It is of interest to investigate the ability of QL in exploring and handling the uncertainties of a dynamic nonlinear system without prior experience of the system. The algorithm applies step-to-step learning to estimate the effect of the system upsets and seek for the best path to optimize the process. On the other hand, monitoring on concentration of ethanol is needed to prevent the quality of yeast from deterioration with the increasing ethanol concentration. The performance of the proposed controller is evaluated based on the boosting of the amount of final yeast produced and the disturbance rejection.

Industrial fed-batch yeast fermentation process is a typical highly nonlinear dynamic process that requires good controlling technique and monitoring to achieve optimization. Due to the various uncertainties in the process, optimization to get the desired product quality with less capital and without compromising the environment is therefore, and has always been an issue of interest in chemical process control. This research aims to report an optimization in substrate feeding to maximize the yeast production and to minimize the production of ethanol under the influence of process disturbance using Q-learning (QL) algorithm. QL is a form of reinforcement learning. It is of interest to investigate the ability of QL in exploring and handling the uncertainties of a dynamic nonlinear system without prior experience of the system. The algorithm applies step-to-step learning to estimate the effect of the system upsets and seek for the best path to optimize the process. On the other hand, monitoring on concentration of ethanol is needed to prevent the quality of yeast from deterioration with the increasing ethanol concentration. The performance of the proposed controller is evaluated based on the boosting of the amount of final yeast produced and the disturbance rejection.

This paper aims to determine the optimal substrate feeding that is able to give high yeast production and low alcoholic content. However, to develop such control system, the complex nature of the yeast metabolism that will affect the system stability has to be considered. Q-learning (QL) is implemented to optimize the yeast production. The main purpose of QL is to reduce the dependency of bioprocess control on human expertise and the amount of data needed for effective control. QL has the learning ability that builds up experience of the process by interacting with the process environment whereby the optimal route seeks through the reward and penalty calculation. In this work, the optimal trajectory suggested by QL was also compared to the exponential feeding rate and their concentration profiles. The performance of the QL controller in rejecting disturbance is also shown.

Exothermic batch reactions are highly nonlinear and complex processes. During the mixing of the chemical reactants, a sudden and unpredictable heat is released causing exothermic reaction. As a result, the runaway reaction may affect the product quality and pose safety problem to the system. Therefore, it is crucial to control the reactor temperature during the process. Often, good controller requires a precise mathematical model in order to achieve accurate control. However, a precise process kinetic model is not easy to obtain since the process dynamic is changing rapidly. Hence, this paper aims to propose Q-learning (QL) algorithm to observe the process behaviour and to estimate the process parameters through the learning ability. The estimated process parameters are then sent to the genetic algorithm (GA) in order to tune a PID controller through evolutionary process. The results show that the proposed method produces satisfactory results in controlling the reactor temperature