Evidence map›Paper›PMID 41754650›Full record

ArticlePolymers2026

Adaptive Integral Sliding Mode Control for Temperature Regulation in Gas-Phase Ethylene Polymerization Fluidized Bed Reactors.

Nayef Ghasem

Abstract read
In one paragraph

Article in Polymers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Nayef GhasemDepartment of Chemical and Petroleum Engineering, United Arab Emirates University, Al Ain 15551, United Arab Emirates.ORCID 0000-0002-1459-1847

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Temperature regulation of gas-phase ethylene polymerization fluidized bed reactors (FBRs) is challenging due to strong nonlinearities, highly exothermic reaction kinetics, and frequent process disturbances. Conventional Proportional-Integral-Derivative (PID) control often exhibits limited robustness under such conditions, while advanced strategies such as Nonlinear Model Predictive Control (NMPC) may suffer from sensitivity to model mismatch and disturbances. In this study, an Adaptive Integral Sliding Mode Control (AISMC) strategy is proposed for temperature control of nonlinear gas-phase FBRs. The controller integrates adaptive gain adjustment with an integral sliding surface to improve disturbance rejection and steady-state accuracy while mitigating chattering. The performance of the proposed approach is evaluated through closed-loop simulations over an 18 h dynamic operating scenario involving multiple setpoint changes, catalyst activity variations, and feed flow disturbances. Simulation results demonstrate that AISMC achieves the best overall tracking performance, with a mean absolute error (MAE) of 0.092 K and the lowest maximum temperature deviation among the evaluated controllers. Compared to PID (MAE = 0.794 K) and conventional sliding mode control (MAE = 0.179 K), AISMC provides substantial improvements in transient and steady-state behaviors. In contrast, NMPC exhibits degraded tracking performance (MAE = 0.809 K) under the considered disturbance conditions. All controllers demonstrate sub-millisecond execution times; however, AISMC attains superior accuracy without excessive computational cost. These results indicate that AISMC offers an effective balance between robustness, accuracy, and real-time feasibility for industrial gas-phase polymerization reactors.

Indexed as

adaptive integral sliding mode controldisturbance rejectionfluidized bed reactorgas-phase polymerizationnonlinear controlreal-time controltemperature control

Identifiers

PMID41754650
PMCPMC12944317

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.