Multi-objective optimization of simulated countercurrent moving bed chromatographic reactor for oxidative coupling of methane

Importance of Process Variables and Their Optimization for Oxidative Coupling of Methane (OCM)

Oxidative coupling of methane (OCM) is a promising process for converting natural gas into high-value chemicals such as ethane and ethylene. The process, however, requires important improvements for commercialization. The foremost is increasing the process selectivity to C₂ (C₂H₄ + C₂H₆) at moderate to high levels of methane conversion. These developments are often addressed at the catalyst level. However, optimization of process conditions can lead to very important improvements. In this study, a high-throughput screening (HTS) instrument was utilized for La₂O₃/CeO₂ (3.3 mol % Ce) to generate a parametric data set within the temperature range of 600-800 °C, CH₄/O₂ ratio between 3 and 13, pressure between 1 and 10 bar, and catalyst loading between 5 and 20 mg leading to space-time between 40 and 172 s. Statistical design of experiments (DoE) was applied to gain insights into the effect of operating parameters and to determine the optimal operating conditions for maximum production of ethane and ethylene. Rate-of-production analysis was used to shed light on the elementary reactions involved in different operating conditions. The data obtained from HTS experiments established quadratic equations relating the studied process variables and output responses. The quadratic equations can be used to predict and optimize the OCM process. The results demonstrated that the CH₄/O₂ ratio and operating temperatures are key for controlling the process performance. Operating at higher temperatures with high CH₄/O₂ ratios increased the selectivity to C₂ and minimized CO_x (CO + CO₂) at moderate conversion levels. In addition to process optimization, DoE results also allowed the flexibility of manipulating the performance of OCM reaction products. A C₂ selectivity of 61% and a methane conversion of 18% were found to be optimum at 800 °C, a CH₄/O₂ ratio of 7, and a pressure of 1 bar.

Multi-Objective Optimisation of Biodiesel Synthesis in Simulated Moving Bed Reactor

In this work, multi-objective optimisation study was performed to determine the performance improvement in a simulated moving bed reactor (SMBR) for biodiesel synthesis. The selection of the operating parameters such as switching time, liquid flow rates in various sections, as well as the length and number of columns is not straightforward in an SMBR. In most cases, conflicting requirements and constraints influence the optimal selection of the decision (operating or design) variables. A mathematical model that predicts single-column experimental results well was modified and verified experimentally for multiple-column SMBR system. In this article, a few multi-objective optimisation problems were carried out for both existing set-up as well as at the design stage. A non-dominated sorting genetic algorithm (NSGA) was used as the optimisation tool for the optimisation study. Due to conflicting effect of process parameters, the multi-objective optimisation study resulted in non-dominated Pareto optimal solutions. It was shown that significant increase in yield and purity of biodiesel in SMBR was possible both for operating and at design stage.

Optimisation of broiler chicken responses from 0 to 7 d of age to dietary leucine, isoleucine and valine using Taguchi and mathematical methods

Three experiments were conducted to evaluate the applicability of the Taguchi method (TM) and optimisation algorithms to optimise the branch chain amino acids (BCAA) requirements in 0 to 7 d broiler chicks. In the first experiment, the standardised digestible (SID) amino acids and apparent metabolisable energy (AME) values of maize, wheat and soya bean meal were evaluated. In the second experiment, three factors including leucine (Leu), isoleucine (Ile) and valine (Val), each at 4 levels, were selected, and an orthogonal array layout of L16 (4(3)) using TM was performed. After data collection, optimisation of average daily gain (ADG) and feed conversion ratio (FCR) were obtained using TM. The multiobjective genetic algorithm (MOGA) and random search algorithm (RSA) were also applied to predict the optimal combination of BCAA for broiler performance. In the third experiment, a growth study was conducted to evaluate the applicability of obtained optimum BCAA requirements data by TM, MOGA and RSA, and results were compared with those of birds fed with a diet formulated according to Ross 308 recommendations. In the second experiment, the TM resulted in 13.45 g/kg SID Leu, 8.5 g/kg SID Ile and 10.45 g/kg SID Val as optimum level for maximum ADG (21.57 g/bird/d) and minimum FCR (1.11 g feed/g gain) in 0- to 7-d-old broiler chickens. MOGA predicted the following combinations: SID Leu = 14.8, SID Ile = 9.1 and SID Val = 10.3 for maximum ADG (22.05) and minimum FCR (1.11). The optimisation using RSA predicted Leu = 16.0, Ile = 9.5 and Val = 10.2 for maximum ADG (22.67), and Leu = 15.5, Ile = 9.0 and Val = 10.4 to achieve minimum FCR (1.08). The validation experiment confirmed that TM, MOGA and RSA yielded optimum determination of dietary amino acid requirements and improved ADG and FCR as compared to Aviagen recommendations. However, based on the live animal validation trial, MOGA and RSA overpredicted the optimum requirement as compared to TM. In general, the results of these studies showed that the TM may be used to optimise nutrient requirements for poultry.