Ganesh Dutta

Hazard remediation and recycling of tea industry and paper mill bottom ash through vermiconversion

Considerable amount of bottom ash (BA) is produced by tea and paper factories in Northeast India. This significantly deteriorates soil and surface water quality through rapid acidification, releasing sulfur compounds and heavy metals. The present investigation endeavoured to convert this waste to organic manure through vermicomposting by Eisenia fetida. Substantial increment in bioavailability of N, P, K, Fe, Mn and Zn along with remarkable decline in toxic metal like Cr due to vermicomposting was noteworthy. Furthermore, vermicomposted mixtures of Tea Factory BA (TFBA) or Paper Mill BA (PMBA) with organic matter (OM) attributed profuse pod yield of French Bean (Phaseolus vulgaris L.). Hence, bioconversion of TFBA and PMBA is highly feasible through vermicomposting and the converted materials can be utilized as potential organic fertilizer.

Optimum covariate designs in split-plot and strip-plot design set-ups

The problem considered is that of finding optimum covariate designs for estimation of covariate parameters in standard split-plot and strip-plot design set-ups with the levels of the whole-plot factor in r randomised blocks. Also an extended version of a mixed orthogonal array has been introduced, which is used to construct such optimum covariate designs. Hadamard matrices, as usual, play the key role for such construction.

Tackling correlated responses during process optimisation of rapeseed meal protein extraction.

Setting of process variables to meet the required specifications of quality characteristics is a crucial task in the extraction technology or process quality control. Simultaneous optimisation of several conflicting characteristics poses a problem, especially when correlation exists. To remedy this shortfall, we present multi-response optimisation based on Response Surface Methodology (RSM)-Principal Component Analysis (PCA)-desirability function approach, combined with Multiple Linear Regression (MLR). Experimental manifestation of the proposed methodology was executed using a multi-responses-based protein extraction process from an industrial waste, rapeseed press-cake. The proposed optimal factor combination reflects a compromise between the partially conflicting natures of the original responses. Prediction accuracy of this new hybrid method was found to be better than RSM alone, verifying the adequacy and superiority of the said approach. Furthermore, this study suggests the feasibility of the exploitation of the waste rapeseed oil-cake for extraction of valuable protein, with improved colour properties using simple, viable process.

D-Optimal Designs for Covariate Parameters in Block Design Set Up

The problem considered is that of finding D-optimal design for the estimation of covariate parameters and the treatment and block contrasts in a block design set up in the presence of non stochastic controllable covariates, when N = 2(mod 4), N being the total number of observations. It is clear that when N ≠ 0 (mod 4), it is not possible to find designs attaining minimum variance for the estimated covariate parameters. Conditions for D-optimum designs for the estimation of covariate parameters were established when each of the covariates belongs to the interval [−1, 1]. Some constructions of D-optimal design have been provided for symmetric balanced incomplete block design (SBIBD) with parameters b = v, r = k = v − 1, λ =v − 2 when k = 2 (mod 4) and b is an odd integer.

D-Optimal Designs for Covariate Models

The problem of finding D-optimal designs in the presence of a number of covariates has been considered in the one-way set-up. This is an extension of Dey and Mukerjee (2006) in the sense that for fixed replication numbers of each treatment, an alternative upper bound to the determinant of the information matrix has been found through completely symmetric C-matrices for the regression coefficients; this upper bound includes the upper bound given in Dey and Mukerjee (2006) obtained through diagonal C-matrices. Because of the fact that a smaller class of C-matrices was used at the intermediate stage where the replication numbers were fixed, ultimately some optimal designs remained unidentified there. These designs have been identified here and thereby the conjecture made in Dey and Mukerjee (2006) has been settled.

Optimum covariate designs in a binary proper equi-replicate block design set-up

The choice of covariates values for a given block design attaining minimum variance for estimation of each of the regression parameters of the model has attracted attention in recent times. In this article, we consider the problem of finding the optimum covariate design (OCD) for the estimation of covariate parameters in a binary proper equi-replicate block (BPEB) design model with covariates, which cover a large class of designs in common use. The construction of optimum designs is based mainly on Hadamard matrices.

Crossover design in clinical trials for binary response

In this paper, we consider a binary response model for the analysis of the two-treatment, two-period and four-sequence crossover design. We have introduced intra-patient drug dependency parameter in the model and provide two tests for the hypothesis of equality of treatment effects. We employ Monte Carlo simulation to compare our tests and a test that works under parallel design on the basis of type I error rate and power. We find that our procedures are dominant over the competitor with respect to power. Finally, we use a data set to illustrate the applicability of our procedure.

Optimum Design for Estimation of Regression Parameters in Multi-Factor Set-Up

The use of covariates in block designs is necessary when the experimental errors cannot be controlled by using only the qualitative factors. The choice of the values of the covariates for a given set-up ensuring minimum variance for the estimators of the regression parameters has attracted attention in recent times. Rao et al. (2003) proposed optimum covariate designs (OCD) through mixed orthogonal arrays for set-ups involving at most two factors where the analysis of variance (ANOVA) effects are orthogonally estimable. In this article, we extended these results and proposed OCDs for the multi-factor set-ups where the factorial effects involving at most t (≤m) factors are orthogonally estimable. It is seen that optimum designs can be obtained through extended mixed orthogonal arrays (EMOA, Dutta et al., 2009a) which reduce to mixed orthogonal arrays for the particular set-ups of Rao et al. (2003). We also proposed constructions of such arrays.

Nearly optimal covariate designs—Part II

ABSTRACT This is a continuation to Part I toward our efforts for providing illustrative examples in the context of analysis of covariance (ANCOVA) models and related analyses of data. We discuss four more examples here, and these are derived from standard textbooks. We re-visit these examples with a view to suggest optimal/nearly optimal designs for estimation of the covariate parameter(s).