Control of a bioprocess is a challenging task mainly due to the nonlinearity of the process, the complex nature of microorganisms, and variations in critical parameters such as temperature, pH, and agitator speed. Generally, the optimum values chosen for critical parameters during Escherichia coli (E.coli) K-12fed-batch fermentation are37 ᵒC for temperature, 7 for pH, and 35 % for Dissolved Oxygen (DO). The objective of this research is to enhance biomass concentration while minimizing energy consumption. To achieve this, an Event-Triggered Control (ETC) scheme based on feedback-feed forward control is proposed. The ETC system dynamically adjusts the substrate feed rate in response to variations in critical parameters. We compare the performance of classical Proportional Integral (PI) controllers and advanced Model Predictive Control (MPC) controllers in terms of bioprocess yield. Initially, the data are collected from a laboratory-scaled 3L bioreactor setup under fed-batch operating conditions, and data-driven models are developed using system identification techniques. Then, classical Proportional Integral (PI) and advanced Model Predictive Control (MPC) based feedback controllers are developed for controlling the yield of bioprocess by manipulating substrate flow rate, and their performances are compared. PI and MPC-based Event Triggered Feed Forward Controllers are designed to increase the yield and to suppress the effect of known disturbances due to critical parameters. Whenever there is a variation in the value of a critical parameter, it is considered an event, and ETC initiates a control action by manipulating the substrate feed rate. PI and MPC-based ETC controllers are developed in simulation, and their closed-loop performances are compared. It is observed that the Integral Square Error (ISE) is notably minimized to 4.668 for MPC with disturbance and 4.742 for MPC with Feed Forward Control. Similarly, the Integral Absolute Error (IAE) reduces to 2.453 for MPC with disturbance and 0.8124 for MPC with Feed Forward Control. The simulation results reveal that the MPC-based ETC control scheme enhances the biomass yield by 7%, and this result is verified experimentally. This system dynamically adjusts the substrate feed rate in response to variations in critical parameters, which is a novel approach in the field of bioprocess control. Also, the proposed control schemes help reduce the frequency of communication between controller and actuator, which reduces power consumption.

Semiconductor wafer discoveries widespread application in unmanned material delivery vehicles, repairing robots, spray androids, and recognition station sensors. the semiconductor cracker’s flaws, such as holes, husks, cuts, and stains that are created during the production process have a negative impact on the quality of products that are produced later. As a result, it was authoritative to inspect wafer defects. Conventional techniques for inspecting semiconductor wafer defects that rely on handcrafted features are limited in some application scenarios and strongly dependent on experience. This research proposes a unique approach for semiconductor wafer surface defect inspection using deep convolutional neural networks. Initially, in order to excerpt features and create feature maps, a innovative structure for feature pyramid networks with atrous convolution (FPNAC) is designed. Secondly, region proposals are generated by feeding the feature plots into the region proposal network (RPN). In order to correctly categorize and segment the flaws, the region recommendations are finally associated to matching size by way of the inputs of a Radial Basis Functional Neural Network (RBFNN), which consists of three branches. The suggested RBFNN produces good overall performance, as evidenced by the experimental findings, which show Mean Intersection over Union (MIoU) of 90.06% and Mean Pixel Accuracy (MPA) of 94.97%.

The present work consists of the elaboration of models for facial recognition of individuals with a mask and to present suggestions of different methodologies that can be applied to adapt existing models. There is a need to evaluate the accuracy of these architectures in real scenarios, especially when it comes to applications that help prevent COVID-19. In this work convolutional neural network is applied to facial recognition. In addition, the ability of transfer learning to assist in the creation of new models will be evaluated. So, the steps include pre-processing of the chosen database, Model development, Training of models with hyperparameter variation, and Validation of models. In a problem of deep convolutional networks with multiclasses, the obtained results were able to classify the vast majority of images and be consistent with the current state of the art.

A bioreactor is a specialized vessel which has the provision of cell cultivation under a sterile environment and the control of the environmental parameters enhances growth. A variety of products related to the food, beverage, and pharmaceutical industries are produced using bioprocesses. Because of the complex dynamics of bioprocesses, controlling them is a difficult and delicate undertaking. Additionally, because of their high nonlinearity, modelling and parameter estimates are made even more challenging. The difficulty of this endeavour is increased by the dearth of online measurements for the biomass and substrate concentrations. The design of an effective controller for any process requires an efficient model. A combination of more than one type of model in a hybrid form can give a better performance. The first principles model is coupled with the data obtained from the real-time bioreactor setup to yield a hybrid model. The process parameters are estimated utilizing a recurrent neural network approach. The developed hybrid model is tested with the real-time bioreactor response and found to be satisfactory.

Introduction: Water scarcity and water pollution are two major issues in India. Circular economy-based wastewater treatment technology provides the most sustainable solutions for solving these issues. In this paper, a novel multi-objective decentralized controller (MODC) is proposed for benchmarking a multi-input multi-output (MIMO) activated sludge wastewater treatment plant (WWTP) to achieve maximum effluent quality with minimum cost. WWTPs with conventional control schemes consume more energy to achieve the desired effluent quality. Methods: In this study, a MIMO model is developed for the activated sludge process (ASP) from a physics-based model, and relative gain array (RGA) analysis are carried out to determine the interaction between the loops to identify a suitable control scheme for the MIMO process. In addition, a multi-objective decentralized control problem is formulated to achieve the conflicting multiple objectives of improving effluent quality and minimizing operational costs by efficient usage of energy. Results and discussion: The desired quality and cost reduction are verified by comparing the integral square error (ISE) and control effort (CE) values of a closed-loop WWTP. A multi-objective evolutionary algorithm (MOEA), namely, the non-dominated sorting genetic algorithm (NSGA)-II, successfully solves the multi-objective control problem. NSGA-II provides several optimal solutions in the Pareto front. In order to demonstrate the feasibility of the proposed controller, three optimal solutions are selected from the Pareto-optimal front, and their closed-loop performances are evaluated qualitatively and quantitatively for both servo and regulatory operations. Improving the quality of effluent enhances active sludge production, which in turn increases the methane production in the anaerobic digester.

Whey fermentation is an important anaerobic fermentation process to produce lactic and alcoholic beverages like ethanol from by-products of cheese and dairy industries using various microorganisms like lactic acid bacteria and yeast. One of the significant parameters affecting the quality and quantity of ethanol yield is Dissolved Oxygen(DO) etc. In this paper, a multi-objective control scheme is proposed for achieving quality and cost of ethanol yield by optimizing DO in fed-batch fermentation of Kluyveromyces Marxianus. Firstly, the DO is controlled by manipulating substrate feed rate with conventional Proportional Integral (PI) controller. Secondly, a multi-objective control problem is formed to optimize PI controller parameters to achieve minimum Integral Error (ISE) to improve quality of ethanol product and minimum control effort to minimize cost of product and successfully solved by NSGA-II. The performance of the proposed controller is analysed under servo and regulatory operations It is found experimentally that the proposed NSGA-II algorithm gives optimal solutions to the DO problem.

Low cost cellulase production has become a major challenge in recent years. The major hurdle in the production of biofuel and other products from biomass is the lack of efficient economically feasible cellulase. This can be achieved by proper monitoring and control of bioprocess. In order to implement any control scheme, the accurate representation of the system in the form of a model is necessary. There are many challenges associated with modeling the fermentation process such as inherent nonlinear dynamic behavior, complexity of process due to co-existence of viable and nonviable cells, presence of solid substrates, etc. Toward the achievement of this goal, researchers have been developing new techniques that can be used to monitor the process online and at-line. These newer techniques have paved the way for designing better control strategies that can be integrated with quality by design (QbD) and process analytic technology (PAT).

This paper presents development of Event Triggered Feed Forward Control (ET-FFC) scheme for K12 Escherichia coli (E.coli) fermentation process. Modeling and control of E.coli fermentation is a challenging task due to strong influence of the risk factors/disturbance like variations in pH, temperature, agitator speed, substrate concentration and air flow rate on biomass yield. The main idea is to develop event triggered Feed forward control scheme to reduce the influence of known disturbance on biomass by minimizing Control Energy(CE) and Integral Square Error (ISE). Real time data of Biomass concentration, substrate feed rate and temperature are collected from the 1.5 Liter laboratory experimental Bioreactor setup. Based on the data, data-driven model based Feedback Controllers (FBC) namely Proportional Integral (PI) controller and Model Predictive controller (MPC) are designed to control Biomass concentration by manipulating substrate feed rate. In addition, Feed-Forward controller is designed for known temperature disturbance. When the temperature disturbance event is detected, FF controller takes control action to suppress its effect on yield. Finally, the closed loop performances of the E.coli fermentation process with proposed controllers are evaluated and analyzed through simulation. Further, a comparative study is carried out for the closed loop system with PI based ET-FFC and proposed MPC based ET-FFC schemes qualitatively as well as quantitatively. The results show that MPC based ET-FFC scheme provides better performances with minimum ISE over PI based ET-FFC scheme.

Bioreactor plays a significant role in many industries such as pharmaceuticals, food products, etc. as these processes depend on the microorganisms. High biomass yield can generally be achieved by operating the bioreactor in fed-batch mode, with an effective model and a suitable advanced control scheme. Modeling a fed-batch bioreactor is a challenging task due to its nonlinear and dynamic behavior. In this work, a hybrid model is developed based on the experimental data collected from a bioreactor that describes the dynamic behavior of aerobic fed-batch cultures of Escherichia coli (E. coli). The biomass profile obtained from hybrid model with GA based feed profile input is used as the desired set point for the Model Predictive Controller (MPC). The parameters of MPC are tuned using Chicken Swarm Optimization (CSO) algorithm. The controller thus designed to obtain maximum biomass concentration uses a predictive model and dynamically updates the feed profile. The real-time automation strategy developed by the authors using LabVIEW (Laboratory Virtual Instrument Engineering Workbench) platform is capable of controlling the key variables such as temperature, Dissolved Oxygen (DO), pH, and antifoam simultaneously during fermentation. The implementation of this optimally tuned controller with optimal set point profile improves the biomass concentration significantly during the fed-batch operation of the bioreactor.

Aims To develop a predictive model for Escherichia coli using deep neural networks. Methods and Results Batch experiments are conducted at different temperatures closer to optimum value (36 center dot 5 degrees C, 37 degrees C, 37 center dot 5 degrees C, 38 degrees C and 38 center dot 5 degrees C) to obtain the growth curves of E .coli K-12. Two primary models namely modified Gompertz and new logistic are chosen. Three secondary models namely Gaussian, nonlinear autoregressive eXogenous (NARX) model and long short-term memory (LSTM) are developed. The novelty in this paper is the development of secondary models using artificial neural network (ANN) and deep network. The performance measures chosen to compare the developed primary and secondary models are correlation coefficient (R-2), root-mean-square error (RMSE) and accuracy factor (A(f)). Results show that modified Gompertz model has better R-2 (0 center dot 99) and RMSE (0 center dot 019) when compared to new logistic model. Also, the deep network model outperforms other secondary models. Based on the primary and novel secondary model, a predictive model (tertiary model) is developed with improved accuracy and is validated. Conclusions The proposed predictive model exhibit good validation results in terms of RMSE and R-2 values and can be applied for determining the growth rate of E. coli at a particular temperature value. Significance and Impact of the Study The proposed model can be used in food processing industries during enzyme production such as Chymosin, to predict the growth rate of E. coli as a function of temperature. Also, the developed LSTM and NARX models can be used to predict maximum specific growth rate of other microbial strains with proper training.

Interest in low cost cellulase production has become a major challenge in recent years for biorefineries. Fed-batch fermentation of Trichoderma strains for the production of low cost cellulase is carried out on complex media that has various soluble and insoluble substrates. The lack of direct estimation of biomass in the presence of insoluble substrates is one of the major concerns for controlling bioprocesses in industries. In this paper, a Multiphase Artificial Neural Network (MANN) based dynamic soft sensor is developed to predict the biomass concentration of Trichoderma during fed batch fermentation in the presence of insoluble substrates. The soft sensor has three Nonlinear Auto Regressive with eXogenous input (NARX) models to capture the complete dynamics of lag, log and stationary phases of the microbe. At different phases, a particular neural network model is triggered based on the period of operation. Each NARX model estimates biomass concentration using online measurements such as pH, substrate concentration and agitation speed. The predicted output of the proposed model and single ANN model are compared against real-time biomass sensor data. The results demonstrated indicate that the proposed MANN based soft sensor shows good performance with focus on the dynamic behavior of the bioreactor. Also, the developed model recursively predicts the biomass concentration with acceptable deviation with respect to realistic measurement. The results summarized could offer a new methodology in estimating fungal biomass accurately, thereby increasing the productivity of cellulase in industries.

Bioreactor imparts a significant role in the manufacture of pharmaceuticals, enzymes, food products, etc. as these processes depend on the biotransformation catalyzed by microorganisms. Dissolved Oxygen(DO) is one of the significant parameter in an aerobic fermentation process. DO control is difficult to achieve due to the variations in process dynamics during batch/fed-batch processes and the complex nonlinear behavior of the Bioreactor. In this paper, design and implementation of Model Reference Adaptive Control(MRAC) scheme based on MIT rule is applied to DO control of the bioreactor using the stirrer speed as control signal. A PC-supported, fully automated, multi-task control system has been designed and built by the authors using LabVIEW. A comparative study is carried out for the experimental bioreactor with conventional PI controller and proposed MRAC scheme for DO control. Results show that MRAC controller provides good tracking performance in comparison to PI controller.

— In this paper, the reduced model of the Pressurized Water Nuclear Reactor (PWR) is derived based on the point kinetics equations and thermal equilibrium relations. The power level of the nuclear reactor is controlled by adjusting the insertion reactivity of the rod. Several controllers such as Genetic Algorithm based PID controller (GAPID), Fractional Order PID controller (FOPID) and Genetic Algorithm based Fractional Order PID Controller (GAFOPID) are used to control the power level of the PWR type of nuclear reactor. The simulation results depict that the Genetic Algorithm based Fractional Order PID Controller (GAFOPID) shows the satisfactory response than other control techniques.

Automation of any process improves process efficiency, yield and minimizes overall cost. Bioreactors place a significant role in pharmaceutical and food processing industries. To utilize the microbes efficiently, the bioreactor should provide optimum conditions such as temperature, pH, oxygen etc. The online monitoring of bioprocess is enhanced by online control of the essential parameters to improve product yield. The effective monitoring of bioprocess is necessary to develop, optimize and maintain biological reactors at maximum efficiency. Furthermore, due to the nature, type and volume of products produced in bioprocesses, there is a strong economic incentive for process monitoring to increase yield and productivity. Most of the bioreactors are supplied with Programmable Logic Controller (PLC)based automation.Most of the PLC supports only PID controllers. The novelty in this paper is the implementation of control algorithms for multiple parameters such as temperature, pH, antifoam and Dissolved Oxygen(DO) using Lab VIEW based automation. The interfacing was done via NI Compact DAQ. Lab VIEW based automation will enhance the performance of the bioreactor with advanced control schemes. The results show that the real-time automation strategy developed in Lab VIEW is capable of simultaneously controlling the temperature, pH, DO and antifoam.

Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced techniques. This paper reviews the recent developments in existing gas sensing technologies and proposes a new advanced system based on embedded logic. The advancement of smart sensor technology has allowed us to design and development of a flexible reliable smart gas detection system to detect gases such as combustible and LPG in the real life. The network consists of four units: a sensor node, a relay node, network coordinator, and a wireless actuator. KeywordsSmart sensor; uninterrupted sensing; Sensor array; sensitivity; selectivity

Wireless Technology based smart sensor networks are becoming predominant from research point of view, since these smart sensors possess the exclusive features like mobility, ad-hoc nature of topology, heterogeneity of nodes, and deployment in huge scales along with the hardship of energy harvesting and routing. In this paper the concern is about the survey of emergence, modification, deployment of sensors in various real time applications where human intervention is risky. KeywordsReliability, Wireless Networks, Energy harvesting.

The impact of unreliable routing, fading and other contingencies in wireless channels can be counteracted by incorporating communication diversity and introducing cooperative paradigms, where third-party nodes contribute to assist the communication. The approaches that are in use today combine both cooperative relaying and coded cooperation. In this article we are introducing a deterministic routing technique to improve the performance of the existing system. We focus on a multiple-input multiple-output ad hoc scenario and show that the improvement brought by cooperative relaying and coded cooperation is not always sufficient; in certain cases the former can be ineffective if no proper relay can be selected, and the latter leads to an overall increase of interference, thus worsening the quality of surrounding links. Therefore, we suggest that along with cooperative relaying and coded cooperation, we are incorporating a new technique to alleviate the problems. Such a joint solution is shown to achieve a significant improvement over the individual approaches. We conclude by discussing future evolutions of the cooperative relaying, coded cooperation and deterministic routing technique and their advanced implementation issues.