Low-density polyethylene injection molding process optimization

Optimizing lipase production of Burkholderia sp. by response surface methodology

Energy use in many process industries is dominated by separation processes. As energy costs are rising rapidly, there is a renewed interest in better methodologies for the synthesis, design and/or retrofitting of separation processes. In this thesis, a novel method for determining energy efficient process designs based on finding the separation with the shortest stripping line distance is proposed. A problem formulation based on mixed integer nonlinear programming (MINLP) is given and a global optimization algorithm is presented for determining energy efficient process designs. A variety of examples of separations involving ideal, non-ideal, azeotropic and reactive mixtures are used to demonstrate the versatility and advantages of the shortest stripping line distance approach over available methods in literature. One of the major advantages of the proposed methodology is that it can be used to identify minimum energy requirement for multi-unit processes such as hybrid separations involving extraction followed by distillation and reaction/separation/recycle processes. The proposed shortest stripping line distance method is extended and a two-level distillation design procedure is developed for finding portfolios of minimum energy designs when specifications are given in terms of key component recoveries. It is shown that the proposed two-level design procedure is flexible and can find minimum energy designs for both zeotropic and azeotropic distillations. It is also shown that the two-level design method encompasses Underwood's solution, when it exists, and can find minimum energy designs when Underwood's method is not applicable. This two-level design approach also overcomes the well-know limitation of distillation line methods of sensitivity of column profiles to the product compositions. Non-pinched, minimum energy distillation designs are an important and often overlooked class of distillation designs that provided added economic advantages in practice. All current methods for designing distillation columns available in literature are based on the concept of pinch points and are incapable of finding non-pinched, minimum energy solutions. In contrast, it is demonstrated that shortest stripping line distance approach is capable of systematically and reliably finding non-pinched, minimum energy distillation designs as well as providing insights into the reasons for the existence of non-pinched, minimum energy design. These reasons include · trajectories that follow unstable branches of a pinch point curve in azeotropic systems, the inherent looping structure of trajectories in hydrocarbon separations, and the presence of ancillary constraints in multi-unit processes like extraction/distillation. Several examples are studied and many numerical results and geometric illustrations are presented in each section show

Maximin distance designs and orthogonal designs are two attractive classes of space-filling designs for computer experiments, but their theoretical constructions are challenging, especially the construction of optimal designs in terms of both the maximin distance and orthogonality criteria. This paper presents a systematic method for constructing orthogonal maximin distance designs with flexible numbers of runs and factors. The method is carried out by rotating the subarrays of a saturated two-level regular design in Yates order or its circular shifting version. The principal objective is to construct high-level designs from two-level designs, and the method is effective because the performance of high-level designs is determined by that of two-level designs under both the maximin distance and orthogonality criteria. The proposed method is also generalized by rotating the subarrays of a saturated two-level nonregular design such that the resulting designs have flexible run sizes. Comparison results reveal that the resulting orthogonal designs are well worthy of recommendation under the maximin distance criterion. An illustrative example is provided to show that the proposed designs have a good two-dimensional stratification property. An application is given to present the effectiveness of the proposed designs in building statistical surrogate models.

Design of experiment is an efficient statistical methodology of establishing which input variables are important (have significant effects) in an experiment (process) and the conditions under which these inputs should work to optimize the outputs of that process. Two-level designs are widely used in high-tech industries and manufacturing for productivity and quality improvement experiments. The construction of (nearly) optimal two-level designs for real-life experiments with large number of input variables can be quite challenging. The practice demonstrated that the existing techniques are complex, highly time-consuming, produce limited types of designs, and likely to fail in large experiments (i.e., optimal results are not expected). To overcome these significant problems, this article gives a simple and effective technique for constructing large two-level designs with good statistical properties. To meet practical needs in different fields, the statistical properties of the generated designs by the new technique are investigated from four basic perspectives: minimizing the similarity among the experimental runs, minimizing the aliasing among the input variables, maximizing the resolution, and filling the experimental domain as uniformly as possible. New recommended saturated orthogonal main effect plans and uniform orthogonal arrays of strength three with thousands or even millions of runs and factors are generated via the new technique without recourse to optimization software.

Comparative study of analytical inductively-coupled argon-plasma discharges using different outer gases

Thermo-economic analysis of integrated membrane-SMR ITM-oxy-combustion hydrogen and power production plant

Optimization of operational parameters of electrocoagulation process for real textile wastewater treatment using Taguchi experimental design method

Design of experiments is an effective, generic methodology for problem solving as well as for improving or optimizing product design and manufacturing processes. The most commonly used experimental designs are two-level fractional factorial designs. In recent years, nonregular fractional factorial two-level experimental designs have gained much popularity compared to the traditional regular fractional factorial designs, because they offer more flexibility in terms of run size as well as the possibility to estimate partially aliased effects. For this reason, there is much interest in finding good nonregular designs, and in orthogonal blocking arrangements of these designs. In this contribution, we address the problem of finding orthogonal blocking arrangements of high-quality nonregular two-level designs in scenarios with two crossed blocking factors. We call these blocking arrangements orthogonal row-column arrangements. We propose two strategies to find row-column arrangements of given two-level orthogonal treatment designs such that the treatment factors’ main effects are orthogonal to both blocking factors. The first strategy involves a sequential approach which is especially useful when one blocking factor is more important than the other. The second strategy involves a simultaneous approach for situations where both blocking factors are equally important. For the latter approach, we propose three different optimization models, so that, in total, we consider four different methods to obtain row-column arrangements. We compare the performance of the four methods by looking for good row-column arrangements of the best two-level 24-run orthogonal designs in terms of the G-aberration criterion, and apply the best of these methods to 64- and 72-run orthogonal designs.

Process optimisation and reaction kinetic model development were carried out for two-stage esterification-transesterification reactions of waste cooking oil (WCO) biodiesel. This study focused on these traditional processes due to their techno-economic feasibility, which is an important factor before deciding on a type of feedstock for industrialisation. Four-factor and two-level face-centred central composite design (CCD) models were used to optimise the process. The kinetic parameters for the esterification and transesterification processes were determined by considering both pseudo-homogeneous irreversible and pseudo-homogeneous first-order irreversible processes. For the esterification process, the optimal conditions were found to be an 8.12:1 methanol to oil molar ratio, 1.9 wt.% of WCO for H2SO4, and 60 °C reaction temperature for a period of 90 min. The optimal process conditions for the transesterification process were a 6.1:1 methanol to esterified oil molar ratio, 1.2 wt.% of esterified oil of KOH, reaction temperature of 60 °C, and a reaction time of 110 min in a batch reactor system; the optimal yield was 99.77%. The overall process conversion efficiency was found to be 97.44%. Further research into reaction kinetics will aid in determining the precise reaction process kinetic analysis in future.

Development and optimisation of a flow injection assay for fluticasone propionate using an asymmetrical design and the variable-size simplex algorithm

Effects of biological pre-digestion of sewage sludge processed by fast pyrolysis on bio-oil yield and biochar toxicity

In this paper, the hydraulic losses of a Kaplan turbine under optimal and rated operating conditions are investigated through the theory of entropy generation. The results show that hydraulic loss values calculated using pressure drop and entropy generation theory are similar. The hydraulic losses are mainly concentrated in runner and draft tube. However, when the operation condition of Kaplan turbines deviates from the optimal condition, the proportion of hydraulic loss in draft tube increases. The hydraulic loss in draft tube is mainly composed of entropy generation caused by turbulence fluctuation, which is the result of unstable flow induced by vortex rope. The hydraulic losses of other parts are mainly composed of entropy production caused by wall shear. The high entropy production of the runner concentrated on the shroud, hub, blade surface, and blade trailing edge, while entropy production caused by wall shear is concentrated on blade pressure surface and shroud near blades when the turbine strays from optimal operating condition.

The aim of this work was to explore the effects of variables on the heat of regeneration, the stripping efficiency, the stripping rate, the steam generation rate, and the stripping factor. The Taguchi method was used for the experimental design. The process variables were the CO2 loading (A), the reboiler temperature (B), the solvent flow rate (C), and the concentration of the solvent (monoethanolamine (MEA) + 2-amino-2-methyl-1-propanol (AMP)) (D), which each had three levels. The stripping efficiency (E), stripping rate ( m ˙ CO 2 ), stripping factor (β), and heat of regeneration (Q) were determined by the mass and energy balances under a steady-state condition. Using signal/noise (S/N) analysis, the sequence of importance of the parameters and the optimum conditions were obtained, and the optimum operating conditions were further validated. The results showed that E was in the range of 20.98–55.69%; m ˙ CO 2 was in the range of 5.57 × 10−5–4.03 × 10−4 kg/s, and Q was in the range of 5.52–18.94 GJ/t. In addition, the S/N ratio analysis showed that the parameter sequence of importance as a whole was A > B > D > C, while the optimum conditions were A3B3C1D1, A3B3C3D2, and A3B2C2D2, for E, m ˙ CO 2 , and Q, respectively. Verifications were also performed and were found to satisfy the optimum conditions. Finally, the correlation equations that were obtained were discussed and an operating policy was discovered.

Water and energy are closely interlinked during their production and consumption processes. The limited and temporary distribution of energy and water resources poses a significant environmental challenge. Industrial wastewater treatment plants are essential elements of water production and also significant energy consumers. This study proposes a methodology for energy management of a wastewater treatment plant. Specifically, it examines the impact of optimum operating conditions on energy costs for a dairy wastewater treatment plant using a dissolved air flotation process. Monte Carlo simulation was used to optimize the parameters and to determine the reuse potential of dairy effluent. Firstly, the optimum operating conditions were determined. The results revealed a maximum fat, oil, and grease removal efficiency of 97% and a chemical oxygen demand removal efficiency of 70%. The optimum conditions were pH of 8, a saturation pressure of 5 bars, and a recirculation ratio of 33%. The optimum concentrations of coagulant and flocculant that contain polyaluminum chloride and cationic polymer were 20 mg/L and 25 mg/L, respectively. The results of the simulation study gave a recirculation ratio of 26.31%, a polyaluminum chloride concentration of 42.5 mg/L, a cationic polymer concentration of 36.31 mg/L, and a saturation pressure of 4.61 bars. Finally, energy cost assessment was performed using a newly developed model which showed that the energy cost indicator of the existing process was lower than optimum operating conditions. The reuse potential of dairy effluent as cooling water was found to be 52%.

FUNDAMENTAL IMPROVEMENT IN THE TRIBOCHARGING SEPARATION PROCESS FOR UPGRADING COAL