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"Proses produksi feronikel di industri menggunakan teknologi Rotary Kiln Electric Furnace (RKEF) yang sangat intensif energi. Penelitian ini bertujuan mengoptimalkan parameter operasional RKEF untuk meningkatkan efisiensi energi sambil menjaga kualitas produk, menggunakan model prediktif Ridge Regression dan algoritma optimisasi. Dua metode utama, yaitu Sequential Least Squares Programming (SLSQP) dan Genetic Algorithm (GA), diterapkan dan dievaluasi berdasarkan efisiensi energi, kepatuhan constraint, kewajaran solusi, akurasi output, dan waktu komputasi. Hasil menunjukkan SLSQP (Skenario e) unggul dalam kepatuhan constraint (0 pelanggaran) dan efisiensi komputasi (23.8 detik), serta menghasilkan solusi input yang lebih wajar (rata-rata outlier 19). Sementara itu, GA (Skenario d) mencapai efisiensi energi yang lebih tinggi (total efisiensi 1.85 Ton/jam) dan akurasi output lebih baik (rata-rata deviasi 4.69), namun dengan kewajaran input yang lebih rendah (rata-rata outlier 40.27) dan waktu komputasi jauh lebih lama (365.78 detik). Berdasarkan pertimbangan stabilitas operasional dan kecepatan, SLSQP dengan skenario e lebih direkomendasikan untuk implementasi industri.

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The ferronickel production process in the industry utilizes the highly energy-intensive rotary kiln electric furnace (RKEF) technology. This study aims to optimize the operational parameters of the RKEF to improve energy efficiency while maintaining product quality, using predictive models and optimization algorithms. Two main methods, namely Sequential Least Squares Programming (SLSQP) and Genetic Algorithm (GA), were applied and evaluated based on energy efficiency, constraint compliance, solution reasonableness, output accuracy, and computation time. Results show SLSQP (Scenario e) excels in constraint compliance (0 violations) and computational efficiency (23.8 seconds), and produces more reasonable solutions (average outlier 19). Meanwhile, GA (Scenario d) achieved higher energy efficiency (total efficiency 1.85 Ton/hour) and better output accuracy (mean deviation 4.69), but with lower input reasonableness (mean outlier 40.27) and much longer computation time (365.78 seconds). Based on operational stability and speed considerations, SLSQP with scenario e is more recommended for industrial implementation."

"Penelitian ini mengoptimalkan konsumsi batubara dalam proses produksi nikel dengan mesin pirometalurgi Rotary Kiln Electric Furnace (RKEF) untuk mengatasi tantangan tingginya biaya operasional dan emisi lingkungan. Tujuan penelitian adalah mengembangkan kerangka kerja optimasi untuk meminimalkan konsumsi batubara total sambil mempertahankan kualitas produk dan target operasional. Kerangka kerja optimasi dikembangkan dengan algoritma optimasi dan dibantu oleh model surrogate yang telah dilatih dari data operasional historis mesin. Optimasi yang dilakukan meliputi empat pendekatan: (1) Sequential Least Squares Programming (SLSQP) objektif tunggal sebagai baseline, (2) R-NSGA-II multiobjektif, (3) optimasi sekuensial dengan SLSQP, dan (4) optimasi sekuensial dengan R-NSGA-II. Eksperimen pada sampel data uji menunjukkan R-NSGA-II objektif tunggal memberikan kinerja terbaik dengan penghematan konsumsi batubara bahan bakar 32.8% dan batubara reduktor 64.1%, sambil meningkatkan kadar nikel 18.3% dan memenuhi 100% batasan operasional. R-NSGA-II sekuensial mencapai penghematan signifikan 22.5% bahan bakar dan 19.2% reduktor dengan kelayakan sempurna, SLSQP objektif tunggal menghasilkan penghematan konservatif 21.0% bahan bakar dengan kelayakan 100%, sedangkan SLSQP sekuensial menunjukkan penghematan minimal 0.10% bahan bakar dan 5.01% reduktor namun tetap mempertahankan kelayakan sempurna. Secara keseluruhan, penelitian ini membuktikan bahwa integrasi model surrogate machine learning dengan algoritma optimasi multi-objektif R-NSGA-II dapat memberikan solusi efektif untuk mengoptimalkan efisiensi energi dan profitabilitas dalam industri pengolahan nikel.

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This research optimizes coal consumption in nickel production using the Rotary Kiln Electric Furnace (RKEF) pyrometallurgical route to address challenges from high operational costs and environmental emissions. The research objective is to develop an optimization framework to minimize total coal consumption while maintaining product quality and operational targets. The optimization framework was developed using optimization algorithms assisted by surrogate models trained from historical operational machine data. The optimization conducted includes four approaches: (1) Sequential Least Squares Programming (SLSQP) single-objective as baseline, (2) R-NSGA-II multi-objective, (3) sequential optimization with SLSQP, and (4) sequential optimization with R-NSGA-II. Experiments on test data samples demonstrate that single-objective R-NSGA-II achieves superior performance with 32.8% fuel coal savings and 64.1% reductor coal savings while simultaneously increasing nickel grade by 18.3% and meeting 100% operational constraints. Sequential R-NSGA-II achieves significant savings of 22.5% fuel and 19.2% reductor with perfect feasibility, single-objective SLSQP provides conservative savings of 21.0% fuel with 100% feasibility, while sequential SLSQP shows minimal savings of 0.10% fuel and 5.01% reductor while maintaining perfect feasibility. Overall, this research demonstrates that the integration of surrogate machine learning models with multi-objective optimization algorithm R-NSGA-II can provide effective solutions for optimizing energy efficiency and profitability in the nickel processing industry. "