Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms

<div>Comparison of <svg height="11.9479pt" id="M110" style="vertical-align:-3.181499pt" version="1.1" viewbox="-0.0498162 -8.7664 14.7935 11.9479" width="14.7935pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M381 665C170 665 44 498 44 318C44 125 188 -15 369 -15C552 -15 703 117 703 333C703 534 550 665 381 665ZM359 629C491 629 601 517 601 306C601 114 502 21 390 21C249 21 146 158 146 346S248 629 359 629Z"></path></g><g transform="matrix(.0091,0,0,-0.0091,9.711,3.132)"><path d="M414 144C384 79 371 75 317 75H135L276 221C367 316 408 376 408 465C408 570 327 635 237 635C179 635 131 609 100 575L42 494L67 471C94 510 138 565 205 565C277 565 321 517 321 435C321 348 258 270 195 195C146 137 88 81 33 26V0H411C423 44 433 88 446 135L414 144Z"></path></g></svg> concentration before and after optimization.</div>

International Journal of Intelligent Systems

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Figure 24

Figure 24: Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms 

Figure 24 | Multiobjective Optimization of Diesel Particulate Filter Regeneration Conditions Based on Machine Learning Combined with Intelligent Algorithms 