An Intelligent Deep Learning Model for <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-4.199599pt" id="M1" height="15.754pt" version="1.1" viewBox="-0.0657574 -11.5544 31.1021 15.754" width="31.1021pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g190-68" d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"/></g><g transform="matrix(.017,0,0,-0.017,11.532,0)"><path id="g190-80" 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"/></g><g transform="matrix(.012,0,0,-0.012,24.441,4.134)"><path id="g50-51" 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"/></g></svg> Adsorption Prediction

<div>Representation of the data statistics for the input <svg height="11.9348pt" id="M75" style="vertical-align:-3.18146pt" version="1.1" viewbox="-0.0498162 -8.75334 23.5622 11.9348" width="23.5622pt" 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="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"></path></g><g transform="matrix(.013,0,0,-0.013,8.736,0)"><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,-0.0091,18.516,3.132)"><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> adsorption) versus the target output (pores volume) (data is collected from the dataset in [<a href="/journals/ast/2022/8136302/#B7">7</a>, <a href="/journals/ast/2022/8136302/#B8">8</a>]).</div>

Adsorption Science & Technology

fig4

Figure 4

Figure 4: An Intelligent Deep Learning Model for <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-4.199599pt" id="M1" height="15.754pt" version="1.1" viewBox="-0.0657574 -11.5544 31.1021 15.754" width="31.1021pt"><g transform="matrix(.017,0,0,-0.017,0,0)"><path id="g190-68" d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"/></g><g transform="matrix(.017,0,0,-0.017,11.532,0)"><path id="g190-80" 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"/></g><g transform="matrix(.012,0,0,-0.012,24.441,4.134)"><path id="g50-51" 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"/></g></svg> Adsorption Prediction 

Adsorption Science & Technology

An Intelligent Deep Learning Model for Adsorption Prediction

Figure 4