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Sharma R.C.,Indian Statistical Institute | Sharma R.C.,Rashtriya Chemicals and Fertilizers Ltd | Banik P.,Indian Statistical Institute
Agroecology and Sustainable Food Systems | Year: 2015

The study was carried out at the Experimental Farm of the Indian Statistical Institute, Giridih, India during the winter seasons of 2007–2008 and 2008–2009 in a split-plot design with three replications. Four legume species (chickpea, pea, groundnut, and lentil) were intercropped with baby corn (Zea mays L.) in 2:1 and 2:2 row arrangements in an additive series besides their sole stand in main-plots and three weeding (no-, one-, and two-weedings) treatments in subplots. Sole baby corn and legumes produced higher economic and by-product yields than their intercropping system. The yield of baby corn declined by 5–14.4% and that of legumes by 8.3–44% when they were grown in association. Intercropping systems had higher baby corn equivalent yield, land use efficiency (28.9–47.2%), area-time efficiency (2.7–15.3%), and monetary advantages, especially in 2:2 row ratios. Baby corn + pea (2:2) followed by baby corn + chickpea (2:2) seemed to be the best intercropping systems in terms of yield advantages and economic returns. Weeding increased yields by reducing competition and yield losses thereby increasing land efficiency and productivity. Intercropping improved soil health measured in terms of NPK, organic carbon, cation exchange capacity, soil enzymes, microbial respiration, and microbial biomass carbon. © Taylor & Francis Group, LLC. Source


Ganesh B.,Indian Institute of Chemical Technology | Kumar V.V.,Indian Institute of Chemical Technology | Kumar V.V.,Rashtriya Chemicals and Fertilizers Ltd | Rani K.Y.,Indian Institute of Chemical Technology
IEEE Transactions on Neural Networks and Learning Systems | Year: 2014

A neural network architecture incorporating time dependency explicitly, proposed recently, for modeling nonlinear nonstationary dynamic systems is further developed in this paper, and three alternate configurations are proposed to represent the dynamics of batch chemical processes. The first configuration consists of L subnets, each having M inputs representing the past samples of process inputs and output; each subnet has a hidden layer with polynomial activation function; the outputs of the hidden layer are combined and acted upon by an explicitly time-dependent modulation function. The outputs of all the subnets are summed to obtain the output prediction. In the second configuration, additional weights are incorporated to obtain a more generalized model. In the third configuration, the subnets are eliminated by incorporating an additional hidden layer consisting of L nodes. Backpropagation learning algorithm is formulated for each of the proposed neural network configuration to determine the weights, the polynomial coefficients, and the modulation function parameters. The modeling capability of the proposed neural network configuration is evaluated by employing it to represent the dynamics of a batch reactor in which a consecutive reaction takes place. The results show that all the three time-varying neural networks configurations are able to represent the batch reactor dynamics accurately, and it is found that the third configuration is exhibiting comparable or better performance over the other two configurations while requiring much smaller number of parameters. The modeling ability of the third configuration is further validated by applying to modeling a semibatch polymerization reactor challenge problem. This paper illustrates that the proposed approach can be applied to represent dynamics of any batch/semibatch process. © 2012 IEEE. Source


Tatwawadi G.N.,Rashtriya Chemicals and Fertilizers Ltd | Shankar P.,Rashtriya Chemicals and Fertilizers Ltd
Chemical Engineering World | Year: 2012

The ammonia plants at RCF Thal were revamped phase wise such as to increase capacity and reduce specific energy consumption. This article describes the approach towards revamp, execution methodology and the results obtained. This revamp is aimed to contribute towards increasing urea availability while reducing dependence on imports. Source

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