Lignin Phenol Formaldehyde Resins Synthesised Using South African Spent Pulping Liquor

Purpose The current study investigated to which extent phenol could be replaced by lignins to produce lignin phenol formaldehyde (LPF) resins, utilising soda lignin and sodium lignosulphonate as by-products from the South African pulping industry.Method The lignins were characterised and soda lignin indicated the highest reactivity. It was therefore utilised to produce LPF resins at 60%, 80%, and 100% phenol substitution, using central composite designs to maximise the adhesive strength. A one-pot method allowing direct transition from phenolation to resin synthesis was used for the first time with a pulping lignin at 60% and 80% substitution.Results Plywood made with LPF60, LPF80, and LPF100 resins attained their highest shear strengths of 0.786, 1.09, and 0.987 MPa, respectively, which adhered to the GB/T 14732-2013 standard (≥ 0.7 MPa). A substitution level of 68% produced the highest shear strength of 1.11 MPa. High-density particleboard made with this LPF68 resin gave a MOR and MOE of 40 and 3209 MPa, respectively, adhering to the ANSI A208.1 requirements. Thickness swelling and water absorption was 13.5% and 37.2%, respectively.Conclusion The soda-lignin isolated by precipitation from sugarcane bagasse pulping liquor is the first industrial lignin shown to produce LPF100 resins adhering to standard requirements, without modification or additives.

Design Efficiency and Optimal Values of Replicated Central Composite Designs with Full Factorial Portions

Efficiency and optimal properties of four varieties of Central Composite Design, namely, SCCD, RCCD, OCCD and FCCD and having r_f replicates of the full factorial portion, r_α replicates of the axial portion and r_c replicates of the center portion are studied in four to six design variables. Optimal combination,[r_f: r_α: r_c ] of design points associated with the three portions of each central composite design is presented. For SCCD, the optimal combinations resulting in A- and D- efficient designs generally put emphasis on replicating the center portion of the SCCD. However, replicating the center and axial portions allows for G-optimal and efficient designs. For RCCD, the optimal combinations resulting in A- and D- efficient designs generally put emphasis on replicating the factorial and center portions of the RCCD. However, replicating the center and axial portions allows for G-optimal and efficient designs. For OCCD, the optimal combinations resulting in A- optimal and efficient designs generally put emphasis on replicating the axial and center portion of the OCCD. The optimal combinations resulting in G- optimal and efficient designs generally put emphasis on replicating the factorial and axial portions of the OCCD. To achieve designs that are D-optimal and D-efficient, the optimal combination of design points generally put emphasis on replicating the center portion of the OCCD. For FCCD, the optimal combinations of design points resulting in A-efficient designs put emphasis on replicating the axial portion of the FCCD. The optimal combinations resulting in G- optimal and efficient designs as well as G-optimal and efficient designs generally put emphasis on replicating the factorial and axial portions of the FCCD. It is interesting to note that for FCCD in five design variables, any r^th complete replicate of the distinct design points of the combination [r_f: r_α: r_c ] resulted in a D-efficient design. Many super-efficient designs having efficiency values greater than 1.0 emerged under the D-criterion. Unfortunately, these designs did not perform very well under A- and G-criteria, having some efficiency values much below 0.5 or just about 0.6.

Evaluation and Comparison of Three Classes of Central Composite Designs

Three classes of Central Composite Design: Central Composite Circumscribed Design (CCCD), Central Composite Inscribed Design (CCID) and Central Composite Face-Centered Design (CCFD) in Response Surface Methodology (RSM) were evaluated and compared using the A-, D-, and G-efficiencies for factors, k, ranging from 3 to 10, with 0-5 centre points, in other to determine the performances of the designs under consideration. The results show that the CCDs (CCCD, CCFD and CCID) are at their best when the G-efficiency is employed for all the factors considered while the CCID especially behaves poorly when using the A- and D-efficiencies.

Missing Observations in Split-Plot Central Composite Designs: The Loss in Relative A-, G-, and V- Efficiency

The trace (A), maximum average prediction variance (G), and integrated average prediction variance (V) criteria are experimental design evaluation criteria, which are based on precision of estimates of parameters and responses. Central Composite Designs(CCD) conducted within a split-plot structure (split-plot CCDs) consists of factorial (𝑓), whole-plot axial (𝛼), subplot axial (𝛽), and center (𝑐) points, each of which play different role in model estimation. This work studies relative A-, G- and V-efficiency losses due to missing pairs of observations in split-plot CCDs under different ratios (d) of whole-plot and sub-plot error variances. Three candidate designs of different sizes were considered and for each of the criteria, relative efficiency functions were formulated and used to investigate the efficiency of each of the designs when some observations were missing relative to the full one. Maximum A-efficiency losses of 19.1, 10.6, and 15.7% were observed at 𝑑 = 0.5, due to missing pairs 𝑓𝑓, 𝛽𝛽, and 𝑓𝛽, respectively, indicating a negative effect on the precision of estimates of model parameters of these designs. However, missing observations of the pairs- 𝑐𝑐, 𝛼𝛼, 𝛼𝑐, 𝑓𝑐, and 𝑓𝛼 did not exhibit any negative effect on these designs' relative A-efficiency. Maximum G- and Vefficiency losses of 10.1,16.1,0.1% and 0.1, 1.1, 0.2%, were observed, respectively, at 𝑑 = 0.5, when the pairs- 𝑓𝑓, 𝛽𝛽, 𝑐𝑐, were missing, indicating a significant increase in the designs' maximum and average variances of prediction. In all, the efficiency losses become insignificant as d increases. Thus, the study has identified the positive impact of correlated observations on efficiency of experimental designs. Keywords: Missing Observations, Efficiency Loss, Prediction variance

Second Order Rotatable Designs of Second Type Using Central Composite Designs

Kim [1] introduced rotatable central composite designs of second type with two replications of axial points for 2≤v≤8 (v: number of factors). In this paper we have extended the work of Kim [1] for second order rotatable designs of second type using central composite designs for 9≤v≤17.

Modified-release of encapsulated bioactive compounds from annatto seeds produced by optimized ionic gelation techniques

To compare the encapsulation of annatto extract by external gelation (EG) and internal gelation (IG) and to maximize process yield (% Y), two central composite designs were proposed. Calcium chloride (CaCl2) concentration (0.3–3.5%), alginate to gelling solution ratio (1:2–1:6); acetic acid (CH3COOH) concentration (0.2–5.0%) and alginate to gelling solution ratio (1:2–1:6) were taken as independent variables for EG and IG respectively. Release studies were conducted under different conditions; morphology, particle size, the encapsulation efficiency (EE), and release mechanism were evaluated under optimized conditions. The optimized EG conditions were 0.3% CaCl2 and 1:1.2 alginate to gelling solution ratio, whereas a 0.3% CH3COOH and 1:5 alginate to gelling solution ratio were optimized conditions for IG. When 20% extract was employed, the highest EE was achieved, and the largest release was obtained at a pH 6.5 buffer. The Peppas–Sahlin model presented the best fit to experimental data. Polyphenol release was driven by diffusion, whereas bixin showed anomalous release. These results are promising for application as modulated release agents in food matrices.