Optimal Set-point Research Articles

Enhancing the Performance of a Simulated WWTP: Comparative Analysis of Control Strategies for the BSM2 Model

This study aimed to improve the performance of a wastewater treatment plant (WWTP) simulated with Benchmark Model No. 2 (BSM2). To achieve this objective, three control strategies were implemented and tested. The first control strategy aimed to maintain the concentration of nitrate and nitrite nitrogen (SNO) by controlling the external carbon flowrate (strategy A1), and the second control strategy aimed to maintain the ammonia and ammonium nitrogen (SNH) at a desired level with the use of a cascade controller (strategy A2). The third strategy was applied to control the total suspended solids (TSS) (strategy A3). Combinations of these strategies were considered (B1, B2, and B3 strategies), as well as the use of all three together (strategy C1). The control strategies presented in this paper were compared to the default control strategy of BSM2 to validate and identify the one that provided the best performance. The results revealed that the B1 strategy was the most environmentally friendly, while C1 obtained the highest overall performance. Several Monte Carlo simulations were performed for the validated control strategies, to identify the optimal setpoint values. For the C1 strategy, a second method of optimization regarding polynomial interpolation was considered. The applied optimization methods provided the optimal reference values for the PI (proportional integral) controllers.

Joint Watermarking-Based Replay Attack Detection for Industrial Process Operation Optimization Cyber-Physical Systems

This paper addresses the replay attack detection issue for a class of industrial process operation optimization (IPOO) cyber-physical systems (CPSs). In contrast to conventional CPSs with a single loop structure, the IPOO CPSs employ a dual-layer network environment consisting of a wireless network for the setpoint optimization loop and a CAN bus for the device control loop. Consequently, the issue of attack detection is more complicated for the IPOO CPSs, and this motivates our current research. First, a unified model of the IPOO CPS is built, with PI controller controlling the physical plants and LQG controller managing setpoint optimization. Then, a novel joint watermarking detection mechanism is established with the PI controller watermarking, LQG controller watermarking, and a watermarking compensator. The proposed watermarking compensator with an augmented Kalman filter is utilized to efficiently eliminate false alarms brought on by information interactions of the coupled cyber-layers, enabling the accurate detection and location of replay attacks. Furthermore, a linear relationship is established between watermarking parameters and system performance loss. Finally, simulations with a quadruple water tank system are conducted to verify the effectiveness of the proposed algorithm.

Hybrid evolutionary robust optimization-based optimal control for time-delay nonlinear systems

Optimal controls receive much attention owing to their remarkable performance for nonlinear systems. However, unknown time-delay disturbances in the optimal control process make it difficult to find high-quality optimal set points. To address this problem, a hybrid evolutionary robust optimization-based optimal control (HERO-OC) method is proposed to reduce the negative effect of time-delay disturbances and enhance the performance of optimal set points. First, a data-driven time-delay disturbance observer (DTDO), based on fuzzy neural networks, is designed to describe the time-delay disturbances of optimal objectives. Then, the expressions and intervals of time-delay disturbances can be estimated to improve the accuracy of optimal objectives. Second, a hybrid evolutionary robust optimization (HERO) algorithm, which combines an expectation robust optimization strategy with a min–max evolutionary robust optimization strategy, is proposed to solve optimal set points. Then, the average robustness and conservative of robustness optimal set points can be improved. Third, an adaptive time-delay controller using Lyapunov–Krasovskii functionals is proposed to track optimal set points. Then, control accuracy is improved while ensuring control stability. Finally, the superior performance of HERO-OC is compared with some novel optimal control methods in a second-order nonlinear system and a wastewater treatment process.

An intelligent monitoring model for greenhouse microclimate based on RBF Neural Network for optimal setpoint detection

The greenhouse climate is a complex dynamic system that requires careful optimization to achieve maximum plant growth with minimum energy consumption. The energy used by greenhouse (GH) actuators has an indirect but detrimental effect on profits. In multi-input/multi-output systems, conventional proportional–integral–derivative (PID) controllers are unable to determine the desired setpoints for the greenhouse microclimate. In this study, an innovative approach to monitoring the greenhouse microclimate is proposed, the Guidance Control Unit (GCU), which optimizes the setpoints of PID controllers for energy efficiency. The proposed system goes through two stages. The first uses Multi-Objective Optimization (MOO) to minimize greenhouse energy consumption based on optimal setpoints generated using the non-dominating sorting genetic algorithm (NSGA-II). The second stage replaces the MOO with a machine-learning approach — the Radial Basis Function Neural Network (RBFNN). After training and testing the RBFNN offline on the generated data, it was used to detect the optimal setpoints for the PID controllers in any weather conditions. The resulting setpoints represent two critical climatic variables, indoor temperature and relative humidity, and are provided to the PID controllers with values that take account of external disturbances. The proposed system showed good detection of the optimal setpoints and maintained the greenhouse system’s energy efficiency at over 25.88%, the overshoot at less 10.92% and rise time showed 11.27 % with an average RMSE of 0.069 for the RBFNN. There was only a minor (0.0017%) difference in prediction error between the results using RBFNN and NSGA-II. The proposed system fulfilled the study’s objectives by detection the optimal setpoints of PID controllers while saving energy and can also be applied to other complex systems, such as industrial buildings, that require more than one optimization step.

Model-Based Decision Support System for Electric Arc Furnace (EAF) Online Monitoring and Control

In this work, a practical approach for a decision support system for the electric arc furnace (EAF) is presented, with real-time heat state monitoring and control set-point optimization, which has been developed within the EU-funded project REVaMP and applied at the EAF of Sidenor in Basauri, Spain. The system consists of a dynamic process model based on energy and mass balances, including thermodynamic calculations for the most important metallurgical reactions, with particular focus on the modelling of the dephosphorisation reaction, as this is a critical parameter for production of high-quality steel grades along the EAF process route. A statistical scrap characterization tool is used to estimate the scrap properties, which are critical for reliable process performance and accurate online process control. The underlying process models and control functions were validated on the basis of historical production and measurement data of a large number of heats produced at the Sidenor plant. The online implementation of the model facilitates the accurate monitoring of the process behaviour and can be applied for exact process end-point control regarding melt temperature as well as oxygen, carbon and phosphorus content. Embedded within a model predictive control concept, the model can provide useful advice to the operator to adjust the relevant set-points for energy and resource-efficient process control.

State machine-based architecture to control system processes in a hybrid fuel cell electric vehicle

This paper presents the development and implementation of a system supervisory controller in a hydrogen-based fuel cell electric vehicle. The controller's primary function is to ensure the safe control of the fuel cell system processes while facilitating coordination among various subsystems, including the balance of plant subsystems, vehicle control unit, diagnosis unit, and powertrain. The supervisory controller comprises of three primary parts: a State Machine, an Optimal Setpoint Generator, and a Power Limit Calculator. The State Machine, which serves as the central part of the supervisory controller, coordinates the fuel cell system's different operational states, including the complex processes of start-up and shutdown. To maximize the fuel cell system's efficiency and minimize the stack's degradation, the Optimal Setpoint Generator produces the subsystem's setpoints by solving an optimization problem and considering the manufacturer's requirements. The Power Limit Calculator assesses the stack's power output capability and calculates the current setpoint for the DC/DC converter. It then provides this data to the Energy Management System (EMS), which oversees the distribution of power between the fuel cell system and the batteries. The proposed fuel cell system supervisory controller is verified using the Worldwide Harmonized Light Vehicles Test Cycles (WLTC) in a real-world car. The designed control structure is implemented in a prototype hydrogen-based electric car at both PowerCell and CEVT facilities under the framework of the INN-BALANCE Horizon 2020 European project.

Demystifying energy savings from dynamic temperature setpoints under weather and occupancy variability

Building temperature setpoints affect both HVAC energy consumption and occupant comfort. To reduce HVAC energy usage, researchers often investigate how system operations can be optimized under weather and occupancy variability subject to a fixed setpoint that minimizes any possible discomfort. While previous research has explored the selection of dynamic setpoints to minimize HVAC energy consumption based on outdoor temperature, they have often neglected the impact of varying occupancy rates on the setpoints. This paper aims to demystify energy savings derived from fixed and dynamic temperature setpoints under weather and occupancy variability and explores the additional energy savings that can be achieved through dynamic temperature setpoints. An exhaustive HVAC zone temperature setpoint optimizer was developed to determine dynamic setpoints with respect to weather and occupancy (i.e., setpoints that minimize HVAC energy consumption at different occupancy rates based on outdoor weather). U.S. DOE reference building energy models for small, medium, and large office buildings were simulated at 17 climate zones, 4 occupancy rates (25%, 50%, 75%, 100%) and 7 setpoints (19.5°C to 25.5°C at 1°C interval). It was found that, both fixed and dynamic setpoints benefit from the energy reduction of approximately 2-4% from the lower heat generated by the occupants at lower occupancy rates. However, at outdoor temperatures between 5°C and 32°C where occupant heat loads can swing the building between heating, free-running, and cooling modes, dynamic setpoints yield additional 2-10% energy savings, compared to fixed setpoints.

Statistically-aided development of protein A affinity chromatography for enhancing recovery and controlling quality of a monoclonal antibody

Protein A chromatography is widely used for isolation of monoclonal antibodies (mAbs) from cell culture components. In this study, the effect of different process parameters of the Protein A purification namely, binding pH, elution pH, flow rate, neutralization pH and tween concentration, on the concentration and quality of the purified mAb were evaluated. Using design of experiments approach, the critical process parameters of protein A chromatography were identified and experimentally optimized. Their impact on quality attributes, such as size variants and charge variants, of the mAb was studied. Multivariate data analysis was subsequently performed using multiple linear regression and partial least squares regression methods. It was observed that the elution pH primarily governed the concentration of the purified mAb and the content of monomers and aggregates, while the tween concentration primarily influenced the main peak of the charge variants. This is the first study that evaluates the impact of tween concentration in buffers on the protein A chromatography purification step. These studies helped in identifying the design space and defining the target robust and optimal setpoints of the responses, which were subsequently verified experimentally. These setpoints not only passed the target criteria but also resulted in the highest recoveries during the investigation. Through this statistically-aided approach, an optimized and robust protein A chromatography process was rationally developed for purification of mAbs, while achieving the desired product quality. This study highlights the influence of multiple parameters of the protein A purification process on critical quality attributes of mAbs, such as the size and charge variants, which has been a very scarcely explored area.

Enhancing the post-combustion carbon dioxide carbon capture plant performance by setpoints optimization of the decentralized multi-loop and cascade control system

Enhancing the post-combustion carbon dioxide carbon capture plant performance by setpoints optimization of the decentralized multi-loop and cascade control system

Experimental validation of multi-stage optimal energy management for a smart microgrid system under forecasting uncertainties

This paper proposes a Multi-stage Energy Management System (MS-EMS) for power distribution in a smart microgrid comprising a photovoltaic system (PV), an Energy Storage System (ESS), and connected to an Electrical Power Grid (EPG). The proposed MS-EMS consists of two layers, the Anticipative Layer (AL) and the Reactive Layer (RL). The AL uses a Multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal power setpoints for each source over the next 24 h based on predicted energy demand and solar power generation. The RL utilizes real-time data collection and Extremum-Seeking optimization (ES) to compensate for prediction uncertainties by adjusting the ideal power setpoints in real-time. In addition, this paper addresses both the issues of management and prediction. It introduces a forecasting system that effectively predicts the next 24 h of load demand and PV power generation using actual data, by integrating a physical model with artificial neural networks (ANN). The efficiency of the proposed MS-EMS is highlighted using experimental data collected in real-time on a physical bench in Morocco. The performances of the proposed MS-EMS system are compared with those of existing EMS strategies, including an existing industrial solution. The comparison shows that the proposed MS-EMS substantially overperforms in terms of three criteria: energy bill, battery degradation cost, and peak-to-Average Ratio (PAR). Despite forecasting uncertainties, the experimental test shows a remarkable 6% gain in the reduction of the energy bill and PAR compared to an offline strategy (where uncertainties were not considered). Furthermore, it achieved a 5% and a 3% gain in the reduction of the energy bill and PAR, respectively compared to an online strategy (focused on real-time optimization). Additionally, compared to an existing industrial solution, the MS-EMS achieves a reduction in energy bill and PAR of 9% and 56% respectively while taking into account battery degradation constraints described in this study.

A novel machine learning-based model predictive control framework for improving the energy efficiency of air-conditioning systems

The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs.

An adaptive optimization method toward batch-wise variable set point of outlet moisture content for the tobacco drying process

The tobacco drying process in the cigarette production has an important effect on the final product quality. Therefore, the intelligent control methods have been widely investigated to ensure the stability of tobacco’s outlet moisture content. The existing work mostly uses a relatively fixed set point of the outlet moisture content for different tobacco batches, which can lead to unforeseen product quality after several processes following the drying process and inaccessible amount of dehydration for the rotary dryer. Some tobacco moisture prediction methods have been studied recently while the relationship with the intelligent control methods remain largely unexplored. To deal with these issues, a novel method is proposed in this paper to identify the optimal set point of the drying outlet moisture content for each tobacco batch. An encoder-decoder model is first developed to forecast the post-drying moisture trajectory. Then, an adaptive filter with specially designed mechanisms and a confidence interval of the dehydration level are constructed to obtain the design constraints. Based on all above, a constrained optimization problem is formulated and solved by the genetic algorithm. Extensive experiments on 895 tobacco batches from a large cigarette factory are carried out, which involves both algorithmic evaluation and field test. It turns out that the proposed method achieves the superior performance and leads to an improvement of the product quality in real production.

Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.

Novel combinator surface concept for efficiency optimization of ship propulsion system

This paper describes a methodology to design and generate a novel optimal combinator surface suitable for mechanical propulsion systems on ships with controllable-pitch propellers (CPPs). The combinator surface is an extension and enhancement of conventional combinator curves, considering the effect of both the advance speed and the propeller thrust for the selection of optimal operation setpoints for propeller RPM and pitch. The generation of optimal combinator surface does not require the estimation of the ship resistance, wake fraction, and thrust deduction fraction, enabling automatic reference setpoint searching during the steady state as well as acceleration and deceleration. The optimization algorithm is created based on the maximization of the power and propulsion efficiency, taking the prime mover and propeller constraints into consideration. The proposed methodology is illustrated using a longliner as a case study ship. From thrust and ship speed analysis, the proposed combinator surface method benefits vessels working with frequently changing operations. The ship propulsion efficiency is improved greatly for ship acceleration. The comparison of the efficiency performance using the conventional combinator curve and the proposed combinator surface demonstrates that the new concept of combinator surface enables more efficient propulsion for ships operating under varying loading and sea state conditions.

Optimal power flow based coordinated reactive and active power control to mitigate voltage violations in smart inverter enriched distribution network

ABSTRACT Voltage violations are the main problem faced in distribution networks (DN) with a higher penetration of inverter-based generations (IBG). Active and reactive power control from smart inverters (SI) can mitigate such violations. Optimal power flow (OPF)-based control provides more accurate operating set points for the coordinated operation of SIs. Therefore, this paper presents a three-phase OPF-based control on SI-enriched unbalanced distribution networks. To consider this, first three-phase model using the current injection model (CIM) is developed. Later, the optimal active and reactive power set points for SIs are obtained by solving a quasi-dynamic optimization problem. The uniqueness of the proposed method is that it regulates the voltage at the affected nodes by obtaining the optimal set points for the smart inverter. The OPF is implemented with a mathematical CIM in Pyomo and solved using the Knitro solver. The proposed method is compared with the sensitivity-based Volt-Var Control (VVC), Volt-Watt Control (VWC), and combined VVC and VWC methods. The effectiveness of the proposed method is verified in a European low-voltage and CIGRE medium-voltage distribution network with 100% penetration. The analysis shows that the OPF-based control optimizes with less network loss and can maintain voltage violations with less reactive power support.

Robust Optimal Control for Wastewater Treatment Process With Uncertain Time Delays

To achieve the excellent operational performance of the wastewater treatment process, optimal control has been considered a reliable method. However, there is a time-delay response of the operation performances to the process variables, leading to uncertainties of operational optimal objectives. It is difficult to obtain the optimal set-points due to the uncertain operational optimal objectives. Therefore, a kernel-density-estimation-based robust optimal control (KDE-ROC) method is proposed. First, a data-driven prediction strategy is developed to construct the uncertain operational optimal objectives. Based on the time-delay intervals, the uncertainties between process variables and operational optimal objectives are expressed. Second, a kernel-density-estimation-based robust optimization algorithm is designed to solve the uncertain operational optimal objectives. Then, the optimal set-points of process variables are obtained depending on the robustness index to reduce the influence of uncertainties. Third, an adaptive neural network controller is developed to track the optimal set-points of process variables. Finally, the proposed KDE-ROC is applied in benchmark simulation model No.1. In the experimental results, the optimal control performance of KDE-ROC is compared with some effective optimal control strategies to demonstrate its effectiveness.

A multi-agent AI reinforcement-based digital multi-solution for optimal operation of a full-scale wastewater treatment plant under various influent conditions

Optimal operating systems have been widely employed to improve the economic and environmental performance of wastewater treatment plants (WWTPs) for efficiently treating discharged water pollutants. However, the multi-optimization approach applied to WWTPs has been inadequate, resulting in a low optimization performance owing to varying influent conditions and inherent complex interactions between manipulated variables. Therefore, this study developed a digital multi-solution for optimal operation of a WWTP based on multi-agent reinforcement learning. Dynamic influent conditions with low, normal, and high influent chemical oxygen demands and total nitrogen composition ratios were generated using a k-means clustering algorithm to accurately reflect operating conditions. A game abstraction method based on a two-stage attention network (G2ANet) algorithm was employed to simultaneously search for three operational setpoints: dissolved oxygen, external sludge recycling, and external carbon dose. The adaptability of the G2ANet-based digital multi-solution for determining setpoints was evaluated by employing newly measured one-year influent data. The results demonstrated the ability of an intelligent G2ANet-based digital multi-solution in identifying optimal setpoints to improve the performance of WWTPs and outperform manual operating systems under varying influent conditions. It reduced aeration energy by 25 % and improved effluent quality by 7 %, while maintaining a similar pumping energy. The G2ANet-based digital multi-solution can innovatively contribute to the smart operation of WWTP operation systems owing to its environmental stability and adaptability.

Evaluation of Heated Window System to Enhance Indoor Thermal Comfort and Reduce Heating Demands Based on Simulation Analysis in South Korea

Heated glass can be applied to improve windows’ condensation resistance and indoor thermal comfort in buildings. Although this applied technology has advantages, there are still some concerns in practical applications, such as additional energy consumption and control issues. This study evaluates the effectiveness of a heated window heating (HWH) system in terms of thermal comfort and heating energy performance (HEP). The simulation-based analysis is performed to evaluate the effectiveness of the HWH using a residential building model and to compare it with radiant floor heating (RFH) and hybrid heating (HH) systems (i.e., combined HWH and RFH). This study also investigates the peak and cumulative heating loads using HWH systems with various scenarios of control methods and setpoint temperature. The predicted mean vote (PMV) is used as an indoor thermal comfort index. The ratio of cumulative thermal comfort time to the entire heating period is calculated. The results show that HWH and HH can reduce the heating load by up to 65.60% and 50.95%, respectively, compared to RFH. In addition, the times of thermal comfort can be increased by 12.55% and 6.98% with HWH and HH, respectively. However, considering the social practices of South Korea, HH is more suitable than HWH. Further investigations for HH show that a surface setpoint of 26 °C is proper, considering both heating demands and thermal comfort. In addition, the setpoint temperature should be determined considering HEP and the thermal comfort for HWH, and the optimal setpoint temperature was suggested under specific conditions.

PH-Based Control of Anaerobic Digestion to Maximise Ammonium Production in Liquid Digestate

A typically overlooked by-product of the anaerobic digestion process is the liquid digestate. The digestate is generally high in valuable nutrients like nitrogen, potassium, and phosphorus, which are essential for plant growth. This indicates that digestate can be an effective fertilizer. In this study, the pH of the anaerobic digestion process was controlled at three different set points (6, 7, and 8) for three different substrates (banana peels, cow dung, and red lentils) in order to determine the ammonium release characteristics at each set point. This was achieved by using two different set-ups; one set-up, named the daily dosing set-up (DDS), incorporated pH corrections once a day, and the other set-up, named the continuous dosing set-up (CDS), corrected the pH every minute. It was discovered that a pH of 7 is the optimal set point for both ammonium release as well as the gas production rate. In terms of a comparative analysis between precise pH control being performed every minute and pH control that was performed once a day, there were differences present in the gas production profiles with the CDS providing enhanced rates compared to the DDS. However, there was a negligible difference in the ammonium release rate.

Synthesis and Soft Implementation of Supervisory Control Scheme in an Industrial Fluid Catalytic Cracking (FCC) Unit of a Nigerian Refinery

In this paper, a self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID(12.6%)/Fuzzy-PID(6%)); settling time (PID(80 seconds)/Fuzzy-PID(20seconds));Integral absolute error(PID(14.29)/Fuzzy-PID(8.63)); integral square error(PID(6.713).Fuzzy-PID(4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)).Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.