The United Nations has set an ambitious goal to achieve net zero carbon emissions by 2050. This objective requires shifting towards green and renewable energy sources instead of conventional fossil fuels to address the global energy crisis without emitting greenhouse gases. While the energy radiated by the sun is one of the most abundant sources of energy available, its efficient and optimal use remains a significant challenge. To facilitate solar-energy-based applications, estimating the amount of solar energy available is crucial. Empirical and soft computing is the most-used method to estimate solar energy. This paper aims to analyze the existing techniques used in various models for estimating and predicting the quantity and quality of solar radiation using readily available data. Additionally, the study aims to identify the most appropriate techniques for developing prediction models using available explanatory variables. To fully harness the potential of solar energy, it is necessary to limit the terrestrial loss of solar radiation by minimizing the harmful effects of anthropogenic factors that reduce the quantity and quality of solar radiation in the area. This paper provides valuable insights to identify opportunities to maximize the potential of solar energy in different locations.

This paper presents identification of artificial neural network model of a Binary Distillation Column (BDC). In this paper, the two most common topologies of artificial neural networks in the area of control are introduced: Feed forward neural network and recurrent neural networks. The training of neural network has been performed by the data set acquired from real 9-tray continuous BDC setup available in laboratory. The network model is composed of two layers. A hyperbolic tangent sigmoid function and a pure linear function have been utilized as activation functions in the first and the second layers, respectively. The developed neural network model has been validated by an extensive data set of practical data received from real BDC setup.

The objective of present research work is to develop a neural network–based model predictive control scheme (NN-MPC) for distillation column. To fulfill this objective, an existing laboratory setup of continuous binary-type distillation column (BDC) is used. An equation-based model that uses the fundamental physical and chemical laws along with valid normal assumptions is validated for this experimental setup. Model predictive control (MPC) is one of the main process control techniques explored in the recent past for various chemical engineering applications; therefore, the conventional MPC scheme and the proposed NN-MPC scheme are applied on the equation-based model to control the methanol composition. In NN-MPC scheme, a three-layer feedforward neural network model has been developed and is used to predict the methanol composition over a prediction horizon using the MPC algorithm for searching the optimal control moves. The training data is acquired by the simulation of the equation-based model under the variation of manipulated variables in the defined range. Two cases have been considered, one is for set point tracking and another is for feed flow disturbance rejection. The performance of the control schemes is compared on the basis of performance parameters namely overshoot and settling time. NN-MPC and MPC schemes are also compared with conventional PID controller. The results show the improvement in settling time with NN-MPC scheme as compared to MPC and conventional PID controller for both the cases.

A fuzzy logic control scheme has been proposed here for binary distillation column. To get the desired composition of products for a binary distillation column, manipulated variables have been adjusted by proposed Fuzzy Inference Systems (FIS). Two separate fuzzy inference systems has been designed to control the top and bottom product composition. The scheme uses fuzzy rules and reasoning to determine the desired outputs based on the error signal and its first difference. The results found by the fuzzy based scheme have been finally compared with the conventional controllers PID and Model Predictive Control (MPC) methods.

In distillation process, composition control is very important. A continuous column has to be operated as precisely as possible to meet the purity specifications. In this work, ANFIS controllers are designed to control the methanol composition in BDC. The controller controls the methanol composition in BDC by the variation of reflux flow-rate and reboiler heat duty. Two separate ANFIS controllers namely: ANFIS A and ANFIS B are designed for the reflux flow-rate and the reboiler heat duty respectively. Each ANFIS controller has three inputs. The performance of this ANFIS control scheme is compared with NN-IMC control scheme on the basis of considered performance indicators (rise time, settling time, MSE). Performance of the ANFIS control scheme is evaluated for reference tracking and disturbance rejection cases. A perturbation of +10 % is incorporated in feed flow-rate for disturbance rejection case. In case of reference tracking, the obtained result shows that there is an improvement of 7 % in the settling time whereas, 14 % improvement in MSE with ANFIS scheme as compared to NN-IMC scheme. In case of disturbance rejection, the obtained result shows that there is an improvement of 14 % in the rise time, 41 % improvement in settling time whereas, 7 % improvement in MSE with ANFIS scheme as compared to NN-IMC scheme.