A greenhouse system (GHS) is a closed structure that facilitates modified growth conditions to crops and provides protection from pests, diseases and adverse weather. However, a GHS exhibits non-linearity due to the interaction between the biological subsystem and the physical subsystem. Non-linear systems are difficult to control, particularly when their characteristics change with time. These systems are best handled with methods of computation intelligence, such as artificial neural networks (ANNs) and fuzzy systems. In the present work, the approximation capability of a neural network is used to model and control sufficient growth conditions of a GHS. An optimal neural network-based non-linear auto regressive with exogenous input (NARX) time series model is developed for a GHS. Based on the NARX model, two intelligent control schemes, namely a neural predictive controller (NPC) and non-linear auto regressive moving average (NARMA-L2) controller are proposed to achieve the desired growth conditions such as humidity and temperature for a better yield. Finally, closed-loop performances of the above two control schemes for servo and regulatory operations are analysed for various operating conditions using performance indices.

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Powerful career planning of employees is reliant on viable execution administration, where administration considers correspondence and reconciliation as a wellspring of keeping employees relentlessly on their approach to objectives accomplishment. The focal point of this investigation, hence, was to survey whether there was career planning and development has its impact on worker inspiration, having a tendency to lose its performing employees work fulfillment and devotion. The present research is a survey on limited scale. It is on the other a descriptive-cum-analytical effort on the selected variables of the study. It is studied how and to what extent the independent variables make changes in the dependent variables. The researcher collected the data from 1000 employees and the sample size is adjusted and determined as 860 respondents to obtain more and clear information. The survey concludes that findings confirm that the model fit is absolutely suitable for this analysis.

Greenhouse system (GHS) is the worldwide fastest growing phenomenon in agricultural sector. Greenhouse models are essential for improving control efficiencies. The Relative Gain Analysis (RGA) reveals that the GHS control is complex due to 1) high nonlinear interactions between the biological subsystem and the physical subsystem and 2) strong coupling between the process variables such as temperature and humidity. In this paper, a decoupled linear cooling model has been developed using a feedback-feed forward linearization technique. Further, based on the model developed Internal Model Control (IMC) based Proportional Integrator (PI) controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to achieve minimum Integral Square Error (ISE). The closed loop control is carried out using the above control schemes for set-point change and disturbance rejection. Finally, closed loop servo and servo-regulatory responses of GHS are compared quantitatively as well as qualitatively. The results implicate that IMC based PI controller using PSO provides better performance than the IMC based PI controller using GA. Also, it is observed that the disturbance introduced in one loop will not affect the other loop due to feedback-feed forward linearization and decoupling. Such a control scheme used for GHS would result in better yield in production of crops such as tomato, lettuce and broccoli.

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Today, in modern large-scale industrial processes, each step-in manufacturing produces a bulk of variables, which are highly precise in nature. However, great challenges are faced under different real-time operating conditions when using just the basic data-driven methods. One of the sultriest research points for convoluted process control is the usage of big data analytics. The aim of big data analytics is to take full advantages of the large amounts of obtained process data and mine helpful details present within. Compared to the well-developed model-based approaches, usage of big data analytics provides productive elective answers for various modern issues under different working conditions. Majority of the modelling in process control in a closed loop system is based on varying the command input to obtain desired controlled output. However, modelling of the process control in a closed loop system based on the disturbance using conventional methods is time consuming since disturbance data is too big and too complex. Utilization of advanced big data analytical methods to mine the disturbance data can lead towards more informed decisions to model the process control in the system. Thus, relevant solutions can be obtained to some of the challenges in the modeling of process control using big data analytics.

Food drying is one of the important methods to prevent microbial growth during preservation. However, it is a complex non-linear process where the quality of the food depends on environmental conditions. Therefore, food drying must be carried out under controlled environment. In this paper, an internal model control (IMC) scheme is developed for pineapple drying using the evolutionary algorithms namely: genetic algorithm (GA) and particle swarm optimization (PSO) to achieve the desired quality (single objective). In order to reduce the control effort and hence the cost, without compromising the desired quality, a multi-objective control scheme is also formulated using weighted sum method. The closed loop performance of the control scheme for GA-based IMC-PI controller and PSO-based IMC-PI controller are analyzed for servo and regulatory operations. The results thus obtained are compared both qualitatively and quantitatively. From the simulation results it is observed that PSO-based IMC-PI controller gives better performance and better range of the temperature compared to the other control schemes.