Online Intelligent Controllers for an Enzyme Recovery Plant: Design Methodology and Performance

This paper focuses on the development of intelligent controllers for use in a process of enzyme recovery from pineapple rind. The proteolytic enzyme bromelain (EC 3.4.22.4) is precipitated with alcohol at low temperature in a fed-batch jacketed tank. Temperature control is crucial to avoid irreversible protein denaturation. Fuzzy or neural controllers offer a way of implementing solutions that cover dynamic and nonlinear processes. The design methodology and a comparative study on the performance of fuzzy-PI, neurofuzzy, and neural network intelligent controllers are presented. To tune the fuzzy PI Mamdani controller, various universes of discourse, rule bases, and membership function support sets were tested. A neurofuzzy inference system (ANFIS), based on Takagi-Sugeno rules, and a model predictive controller, based on neural modeling, were developed and tested as well. Using a Fieldbus network architecture, a coolant variable speed pump was driven by the controllers. The experimental results show the effectiveness of fuzzy controllers in comparison to the neural predictive control. The fuzzy PI controller exhibited a reduced error parameter (ITAE), lower power consumption, and better recovery of enzyme activity

Development And Implementation Of Fuzzy Logic Controller For Flow Control Application

This paper aims to shed some light on the Development and Implementation of Fuzzy Logic Controller for Flow Control Application. The development will be done onto the PcA Simlxpert Mobile Pilot Plant SE231B-21 Flow Control and Calibration Process (Init. This mobile plant contains flow measurement instruments such as Coriolis, Vortex and Orifice Flow meters. Currently, control and tuning is done via KONICS PID Controller that is mounted on the local control pane

A Feasibility Study for the Automated Monitoring and Control of Mine Water Discharges

The chemical treatment of mine-influenced waters is a longstanding environmental challenge for many coal operators, particularly in Central Appalachia. Mining conditions in this region present several unique obstacles to meeting NPDES effluent limits. Outlets that discharge effluent are often located in remote areas with challenging terrain where conditions do not facilitate the implementation of large-scale commercial treatment systems. Furthermore, maintenance of these systems is often laborious, expensive, and time consuming. Many large mining complexes discharge water from numerous outlets, while using environmental technicians to assess the water quality and treatment process multiple times per day. Unfortunately, this treatment method when combined with the lower limits associated with increased regulatory scrutiny can lead to the discharge of non-compliant water off of the mine permit. As an alternative solution, this thesis describes the ongoing research and development of automated protocols for the treatment and monitoring of mine water discharges. In particular, the current work highlights machine learning algorithms as a potential solution for pH control.;In this research, a bench-scale treatment system was constructed. This system simulates a series of ponds such as those found in use by Central Appalachian coal companies to treat acid mine drainage. The bench-scale system was first characterized to determine the volumetric flow rates and resident time distributions at varying flow rates and reactor configurations. Next, data collection was conducted using the bench scale system to generate training data by introducing multilevel random perturbations to the alkaline and acidic water flow rates. A fuzzy controller was then implemented in this system to administer alkaline material with the goal of automating the chemical treatment process. Finally, the performance of machine learning algorithms in predicting future water quality was evaluated to identify the critical input variables required to build these algorithms. Results indicate the machine learning controllers are viable alternatives to the manual control used by many Appalachian coal producers

Macromixing study for various designs of impellers in a stirred vessel

The effect of the impeller designs and impeller clearance level (C/T) on power consumption, mixing time and air entrainment point in a single liquid phase under turbulent conditions (Re > 104) were investigated. Different impeller designs including conventional and new designs, were used to consider both axial and radial flow impellers. The electric conductivity method, suspended motor system and observation method were employed to determine the mixing time, the power consumption and the air entrainment point, respectively. The reduction in the impeller clearance level form T/3 to T/6 resulted in a decrease in power number values for up-flow pumping impellers while it was increased for down-flow pumping. The same trend was observed for the mixing time results. Moreover, axial flow impellers and specially HE3 are preferable for higher agitation speeds due to the less air entrainment. The results verified that the axial flow impellers and specifically down-flow impellers are more efficient than the radial flow impellers. ANFIS-Fuzzy C–means (ANFIS–FcM) and nonlinear regression were used to develop models to predict the mixing time based on the energy dissipation rate and clearance. The results verified that the model predictions successfully fit the experimental mixing time data

Fuzzy Logic for pH Neutralization Process

pH neutralization process is a process that is widely studied due to its highly nonlinear process reaction. Its nonlinearity behavior is caused by static nonlinearity between pH and concentration. This nonlinearity depends on the substances in the solution and on their concentrations. In this project, the nonlinearity of the process was investigated. Later, the mathematical model of the process was developed based on McAvoy et al [I]. In addition to the mathematical model, an empirical model was also obtained from Analytical & Chemical Pilot Plant located in the Process Control & Instrumentation Laboratory (23-00-06). Both models were then used to develop the Fuzzy Logic Controller (FLC) by using Advanced-Neuro Fuzzy Inference System (ANFIS) and also gain-scheduling method. In ANFIS implementation for empirical model, the FLC output was identical to the output from PID. Therefore it is concluded that FLC could be used to replace PID for empirical model. In ANFIS implementation for mathematical model, the FLC also could be implemented for mathematical model since the controlled variable successfully follows all the set point changes. For gainscheduling method, the FLC was tested on servo and regulator problems. The servo test was performed by using a random number generator to generate random pH set points between 3 and 11 and the simulation is performed for 100 seconds. The result for the servo test was similar with the result from the ANFIS implementation for mathematical model. For regulator test, the disturbance was the ±20% variation in acid flow. The result for the regulator shows, the controller manages to eliminate the disturbance effect in the process variable. In overall, the project successfully shows that FLC could be a good alternative to PID controller

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Application of fuzzy logic for power management in hybrid vehicles

The increasing number of cars may be causing serious effects to the environment and to humans, such as pollution, global warming, and depletion of oil reserves, among others. This situation encourages the research for new energy forms and devices with higher energy efficiency. The adoption of hybrid propulsion technology has contributed, considerably, to reducing gases such as oxides of carbon, nitrogen and sulfur and the reduction of particulate materials. Beyond, the hybrid electric vehicle (HEV) maintains the characteristics attributed to conventional vehicles such as performance, safety and reliability. The term "hybrid” derives from the combination of two or more power sources, and the most common combination is through of an internal combustion engine (ICE), commonly used in conventional vehicles, together with the battery and electric motor (EM) used in EVs (Electric Vehicles). In general, the main reason to use electric hybrid architecture is the additional degree of freedom due to the presence of an additional energy source, which implies that, at each instant, the power required by the vehicle can be provided by one of these sources, or a combination of both. Choose the correct combination is usually a complex task. For a HEV present satisfactory operation (performance and emission reduction) is important that the architecture and components of HEVs are optimized, and occurs an appropriate choice of power management strategy. In this work is carried out the development and analysis of power management strategies in a HEV to minimize its fuel consumption and consequently emissions. Is developed one management strategy using fuzzy systems, and its results is analyzed varying the vehicle mass. The results of this work allow to view when it is triggered each propulsion system, and to analyze the consumption of fuel for each power management strategy3324452455CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESNão temNão te

River flow forecasting using an integrated approach of wavelet multi-resolution analysis and computational intelligence techniques

In this research an attempt is made to develop highly accurate river flow forecasting models. Wavelet multi-resolution analysis is applied in conjunction with artificial neural networks and adaptive neuro-fuzzy inference system. Various types and structure of computational intelligence models are developed and applied on four different rivers in Australia. Research outcomes indicate that forecasting reliability is significantly improved by applying proposed hybrid models, especially for longer lead time and peak values