Multi-stage optimization in a pilot scale gasification plant

Silva, Valter; Couto, Nuno; Eusébio, Daniela; Rouboa, Abel; Brito, Paulo; Cardoso, João; Trninić, Marta

doi:10.1016/j.ijhydene.2017.04.261

Cited by 18 publications

(15 citation statements)

References 35 publications

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“…Regarding the CGE, as the air flow rate increases jointly with MSW feeding rate, the CO 2 production comes enlarged which will affect negatively the MSW conversion into product gas (air flow rate = 40 m 3 /h and MSW feeding rate = 25 kg/h) . The obtained response profiles are in agreement with the current literature …”

Section: Resultssupporting

confidence: 88%

“…Adeq‐Precision comparatively addresses the predicted value extent at the design points to the average prediction error. If the ratios result higher than 4, then the model is considered to be suitable …”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the H 2 /CO ratio improves with the air flow increase as it promotes the partial combustion of char, CO, and H 2 (air flow rate = 100 m 3 /h and MSW feeding rate = 75 kg/h) . The MSW feeding rate impacts the carbon conversion negatively, while the airflow will positively impact this response by promoting oxidation reactions leading to exothermic steam reforming reactions which will promote carbon conversion (air flow rate = 100 m 3 /h and MSW feeding rate = 25 kg/h) . Regarding the CGE, as the air flow rate increases jointly with MSW feeding rate, the CO 2 production comes enlarged which will affect negatively the MSW conversion into product gas (air flow rate = 40 m 3 /h and MSW feeding rate = 25 kg/h) .…”

Section: Resultsmentioning

confidence: 99%

“…If the ratios result higher than 4, then the model is considered to be suitable. 25 The higher order polynomial determined by the sequential model sum of squares (SMSS) suggested that the quadratic model provided the best fitting to the experimental data, including linear (A, B), interaction (AB), and quadratic (A 2 , B 2 ) terms to depict the variation of the process. A and B terms suited the ER and CMDR factors, respectively, for the air-CO 2 mixture gasification, while for the air gasification, A and B terms suited the air flow rate and the MSW feeding rate, respectively.…”

Section: Empirical Model Validationmentioning

confidence: 99%

“…The authors studied the potential of several biomasses to generate syngas for multiple applications and verified that the desired operating conditions to achieve improved performances do not consistently meet with the ones required to achieve a stable syngas composition. Lately, Silva et al applied a multiple optimization approach combining DoE and response surface method to optimize MSW gasification. Once the optimization and robustness procedures were effectively implemented, the authors could select the optimal operating conditions to generate improved syngas composition.…”

Section: Introductionmentioning

confidence: 99%

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Process optimization and robustness analysis of municipal solid waste gasification using air‐carbon dioxide mixtures as gasifying agent

2019

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Summary In this work, a computational fluid dynamics (CFD) model was coupled with an advanced statistical strategy combining design of experiments (DoE) and the Monte Carlo method to comparatively optimize and test the robustness of two municipal solid waste (MSW) gasification processes one using air‐carbon dioxide (CO2) mixtures as a gasifying agent and the other using air alone. A 3k full factorial design of 18 computer simulations was performed using as input factors for air‐CO2 mixtures the equivalence ratio and CO2‐to‐MSW ratio, while MSW feeding rate and air flow rate were used for air gasification. The selected responses were CO2, H2, CO, and CnHm generation, CH4/H2 and H2/CO ratios, carbon conversion, and cold gas efficiency (CGE). Findings were that DoE allowed determining the best‐operating conditions to achieve optimal syngas quality. Monte Carlo identified the best‐operating conditions reaching a more stable high‐quality syngas. Air‐CO2 mixture gasification showed enhanced responses with major improvements in CO2 conversion and CGE, both up to a 13% increase. The optimal operating conditions that set the optimized responses showed to not always imply the most stable set of values to operate the system. Finally, this combined optimization process performance revealed to grant professionals the ability to make smarter decisions in an industrial environment.

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Section: Resultssupporting

confidence: 88%

Section: Resultsmentioning

confidence: 99%