Evidence map›Paper›PMID 41792253›Full record

ArticleScientific reports2026

Application of modified multi-verse optimization for temperature control in thermal power plant condensers.

Sweta Panda, Soumya Ranjan Das, Arun Kumar Sahoo, M UmaMaheswar Rao, Norah Saleh Alghamdi, Wattana Viriyasitavat, Gaurav Dhiman

Abstract read
In one paragraph

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

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Sweta PandaDepartment of Electrical Engineering, VSSUT Burla, Burla, Odisha, India.
Soumya Ranjan DasManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. soumya.das@manipal.edu.
Arun Kumar SahooDepartment of Electrical Engineering, IIIT Bhubaneswar, Bhubaneswar, Odisha, India.
M UmaMaheswar RaoDepartment of CGDT/ Design, IIT Hyderabad, Hyderabad, India.
Norah Saleh AlghamdiDepartment of Computer Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Wattana ViriyasitavatDepartment of Statistics, Chulalongkorn University, Bangkok, Thailand.
Gaurav DhimanDepartment of Computer Science and Engineering, Yuan Ze University, Tao Yuan, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The surface condenser exhibits nonlinear dynamics and inherent time delays, making precise temperature regulation essential for stable operation in thermal power systems. In this study, a Modified Multi-Verse Optimizer (MMVO) is employed to tune the parameters of a PID controller for improved temperature control of a shell-and-tube condenser. The methodology involves formulating the PID tuning task as an optimization problem, applying MMVO with defined search bounds, and evaluating its performance using 23 standard benchmark functions and repeated simulation runs. Statistical indicators including best, worst, average, and standard deviation values across 30 independent executions are used to assess robustness. Comparative analyses with Ziegler-Nichols (ZN), Genetic Algorithm (GA), and the original Multi-Verse Optimizer (MVO) demonstrate that the modified approach achieves lower integral error indices and reduced overshoot, while providing more consistent performance across trials. The results indicate that MMVO-based PID tuning offers enhanced control capability for systems with strong nonlinearities and delay characteristics.

Indexed as

GAMVOPID controller tuningSurface condenser

Identifiers

PMID41792253
PMCPMC13083958

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