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

Abstract

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 C 2 (C 2 H 4 + C 2 H 6 ) 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 La 2 O 3 /CeO 2 (3.3 mol % Ce) to generate a parametric data set within the temperature range of 600−800 °C, CH 4 /O 2 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 CH 4 /O 2 ratio and operating temperatures are key for controlling the process performance. Operating at higher temperatures with high CH 4 /O 2 ratios increased the selectivity to C 2 and minimized CO x (CO + CO 2 ) at moderate conversion levels. In addition to process optimization, DoE results also allowed the flexibility of manipulating the performance of OCM reaction products. A C 2 selectivity of 61% and a methane conversion of 18% were found to be optimum at 800 °C, a CH 4 /O 2 ratio of 7, and a pressure of 1 bar.

Olaparib Process Development Employing Quality by Design (QbD) Principles

“…The effects of all process variables were investigated by using a stepwise linear model. The ANOVA method was applied to identify the power factors by calculating the p -value, F ratio, and R square ( R 2 ) …”

“…DoE manipulates each process variable individually to identify invisible links among them, including main, two-way, and three-way interactions. 18 There are many types of experimental designs that can be applied throughout the workflow to establish and control the CPPs. The choices of design should be fit for the intended purpose.…”

“…The ANOVA method was applied to identify the power factors by calculating the p -value, F ratio, and R square ( R 2 ). 18 …”

“…The ANOVA method was applied to identify the power factors by calculating the p-value, F ratio, and R square (R 2 ). 18 The P-value represents a probability that ranges between zero and one and is used to evaluate the significance of each variable. Variables with a p-value less than 0.05 (level of significance) have a high effect.…”

“…The Design of Experiments was developed to consider all possible interactions between variables compared with the traditional OFAT approach. DoE manipulates each process variable individually to identify invisible links among them, including main, two-way, and three-way interactions . There are many types of experimental designs that can be applied throughout the workflow to establish and control the CPPs.…”