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"Penelitian ini mengevaluasi performa algoritma dekomposisi LU dengan batasan serta metode iterasi Conjugate Gradient Method (CGM) dalam menentukan konduktivitas termal aluminium dan besi menggunakan metode Inverse Heat Conduction Problem (IHCP). IHCP digunakan untuk menyelesaikan masalah konduksi panas dengan menentukan parameter yang tidak diketahui seperti kondisi batas dan konduktivitas termal bergantung pada temperatur. Algoritma dekomposisi LU dengan batasan diimplementasikan dalam IHCP 2D untuk mengoptimalkan perhitungan distribusi temperatur. Simulasi pada pelat aluminium menunjukkan kesalahan absolut maksimum 1,22%, sementara eksperimen dengan isolasi penuh menunjukkan 1,83%. Prediksi konduktivitas termal menggunakan tembakan 10, 50, dan 100 W/mK menghasilkan nilai konduktivitas aluminium antara 233,693 hingga 240,659 W/mK dengan kesalahan maksimum 1,83%, dan besi antara 78,84 hingga 80,38 W/mK dengan kesalahan maksimum 1,74%. Kesimpulannya, variasi material, nilai konduktivitas termal, fluks panas, dan kondisi sistem tidak berdampak signifikan pada prediksi konduktivitas termal. Peningkatan peralatan uji dan metode pengukuran yang lebih akurat diperlukan untuk aplikasi praktis.

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This study aims to evaluate the performance of the LU decomposition algorithm with constraints and the Conjugate Gradient Method (CGM) iteration in determining the thermal conductivity of aluminum and iron materials using the Inverse Heat Conduction Problem (IHCP) method. IHCP is applied to solve heat conduction problems, determining unknown parameters such as boundary conditions and temperature-dependent thermal conductivity. In this research, the LU decomposition algorithm with constraints was implemented in a 2D IHCP to optimize forward calculations for temperature distribution. Simulations on aluminum plates showed a maximum absolute error of 1.22%, while experiments with full insulation showed 1.83%. Thermal conductivity prediction using shots of 10, 50, and 100 W/mK revealed values for aluminum ranging from 233.693 to 240.659 W/mK with a maximum error of 1.83%, and for iron from 78.84 to 80.38 W/mK with a maximum error of 1.74%. The study concludes that material variation, thermal conductivity values, heat flux, and system conditions do not significantly impact thermal conductivity prediction. Therefore, more accurate testing equipment and measurement methods are necessary for practical applications."

"Conjugate Gradient merupakan suatu metode optimasi yang dapat meminimasi suatu fungsi, dimana arab pencariannya berdasarkan arab konjugasi yang nilainya ortogonal. Karena sifat pencariannya yang ortogonal, sebingga membuat Conjugate Gradient dapat mencapai konvergensi pad a solusi yang dicari dengan cepat. Conjugate Gradient tidak banya dapat digunakan untuk menyelesakan fungsi yang Iinier, tetapi juga dapat digunakan untuk masalah non Iinier, salab satunya digunakan untuk pelatihan jaringan syaraf tiruan. Pada makalah ini, digunakan algoritma Conjugate Gradient Polak Ribiere dalam pelatiban jaringan syaraf tiruan sebagai sistem peramalan temperature udara. Data k1imatologi yang digunakan sebagai parameter masukan yaitu temperature udara, kelembaban, tekanan udara, curah bujan, lama penyinaran matabari, dan kecepatan an gin. Berdasarkan basil penelitian ini, kita dapat menginterpretasikan babwa sistem peramalan temperatur udara ini mengbasilkan akurasi lebib dari 90%."

"The Lanczos and conjugate gradient (CG) algorithms are fascinating numerical algorithms. This book presents the most comprehensive discussion to date of the use of these methods for computing eigenvalues and solving linear systems in both exact and floating point arithmetic. The author synthesizes the research done over the past 30 years, describing and explaining the "average" behavior of these methods and providing new insight into their properties in finite precision. Many examples are given that show significant results obtained by researchers in the field.The author emphasizes how both algorithms can be used efficiently in finite precision arithmetic, regardless of the growth of rounding errors that occurs. He details the mathematical properties of both algorithms and demonstrates how the CG algorithm is derived from the Lanczos algorithm. Loss of orthogonality involved with using the Lanczos algorithm, ways to improve the maximum attainable accuracy of CG computations, and what modifications need to be made when the CG method is used with a preconditioner are addressed."

"Optimasi biaya bahan bakar umumnya dilakukan dengan pendekatan menggunakan metode deterministi c maupun undeterministi c. Pada penelitian ini membandingkan penerapan merit order yang bersifat deterministi c dengan penerapan algoritma kelelawar yang bersifat undeterministi c. Persoalan economic load dispatch mempunyai batasan equality dan inequality yang kompleks, sehingga sulit menentukan nilai optimum dengan menggunakan pendekatan konvensional. Dalam menentukan nilai optimum diperlukan penjadwalan unit-unit pembangkit untuk membagi daya yang dibangkitkan dalam pemenuhan kebutuhan sistem sehingga didapatkan biaya bahan bakar optimum. Merit order disusun berdasarkan besaran biaya bahan bakar perjam setiap unit yang beroperasi pada output maksimumnya, sedangkan algoritma kelelawar disusun berdasarkan karakteristik ekolokasi kelelawar yang disimulasikan pada program komputer dari posisi, kecepatan dan frekuensi kelelawar. Data yang diuji adalah data aktual pembangkit listrik tenaga termal yang berjumlah 6 (enam) pembangkit pada kondisi beban puncak tahun 2018. Dengan menggunakan 2 (dua) metode yang berbeda yaitu merit order dan algoritma kelelawar diperoleh hasil biaya produksi (efisiensi) yang berbeda. Merit order dapat mengefisienkan biaya produksi sebesar 14,67% atau $291640 dari aktual biaya, sementara algoritma kelelawar menghasilkan efisiensi sebesar 15,66% atau $311405 dari aktual biaya. Dari hasil perhitungan ini dapat disimpulkan bahwa penggunaan metode algoritma kelelawar akan menghasilkan biaya produksi yang lebih efisien (lebih kecil) yaitu sebesar 0,99% atau $19765 dibandingkan metode merit order. Hal ini dapat terjadi karena algoritma kelelawar berhasil membuat kombinasi pembebanan pembangkit yang lebih efisien.

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Fuel cost optimization is generally done using an approach of deterministic and undeterministic methods. This study compares the application of deterministic merit order algorithms with the application of undeterministic bat algorithms. The issue of economic load dispatch has complex equality and inequality constraints, so it is difficult to determine the optimum value using a conventional approach. In determining the optimum value it is necessary to schedule generator units to divide the generated power in meeting system requirements so the optimum fuel costs are obtained. Merit orders are arranged based on the amount of hourly fuel costs per unit operating at its maximum output, while the bat algorithm is based on echolocation characteristics of microbats simulated on a computer program from the position, velocity and frequency of bats. The researched data are the actual data of thermal power plants which amount to 6 (six) plants in the peak loads condition in 2018. By using 2 (two) different method, namely merit order and bat algorithm, the results of different production costs are obtained. The merit order can reduce production costs by 14.67% or $291640 of the actual cost, while the bat algorithm produces an efficiency of 15.66% or $311405 of the actual cost. From the results of this calculation it can be concluded that the use of bat algorithm can produce a more efficient (smaller) generation costs that is equal to $19765 or 0.99% smaller than the merit order method. This can occur because of the bat algorithm manages to create a loading combination of more efficient power plants."

"Applied supply side Management of electric Energy in Supply side. This is prior in order tooptimalize of using Hydro generation so it could operation as maximal as possible in the peak of systemloading. In this WG] eficiency of using minimum fuel as expected can be occur in Operation theThermal Generation.In order to have an optimum .system, in Electric Power System that consist of unit Hydro Generationand other Thermal Generation such as PLTU1 PLTP, PLTGDI PLTG. A loading regulation is necessaryto build in an electrical power generation, in order to have an optimum operation for electric powersystem, in which minimum fuel cost in operation electric energy need all. It is important to do because theelectrical power generation serves electric power loads, that always change by the time, and also resultfuel-cost per unit time in rupiahs pers hours, changes by the time.The cost of Electrical Power System operation is set as the goal to be minimized and the junction ofoperation cost will be change. Gradient Method will be used in order to solve this hind of matters. Theoptimum of generation regulation in this paper was restricted as generation regulation of real powerwhenever the optimum generation using this gradient Method is more profitable then using merit ordermethod technically and economically.In this case Computer program will be Applied using computer simulation in which Borland Delphirelease 7.0 as computer language programming, in order to to find an optimization of operation systemhydro-thermis power generation. As the result of Computer simulation program, is picture, table or chartthat would be easier to be understood."

"ABSTRAKPenelitian ini bertujuan untuk mengoptimumkan konversi TKKS menjadi etanol, furfural, dan listrik yang terintegrasi dengan sistem generasi kukus agar menghasilkan performa ekonomi dan lingkungan yang optimum. Performa ekonomi diukur dengan NPV (net present value) sedangkan performa lingkungan diukur melalui emisi CO2 hasil analisis life cycle. Hasil optimisasi menunjukkan bahwa suhu optimum untuk reaksi hidrolisis adalah 180°C dan pemenuhan fraksi kukus massa dari generasi kukus tenaga surya yang optimum berada pada rentang 0-0,28 yang ditunjukkan oleh kurva Pareto. CSP mampu memenuhi seluruh kebutuhan kukus secara finansial pada pembangunan unit ke-10 dengan proyeksi learning curve. Split fraksi TKKS untuk objektif optimum didapatkan pada fraksi massa TKKS sebesar 0.25 ke unit reaktor hidrolisis.

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ABSTRAKThe purpose of this research is to optimize the conversion process of EFB to ethanol, furfural, and electricity through co-production principal integrated with solar-assisted steam generation system, to achieve optimum economic and environmental performances. Economic performance is measured by NPV, while environmental performance by CO2 emission through life cycle analysis. The multi-objective optimization shows that the optimum temperature of hydrolisis reaction is 180°C and solar-assisted generation system is applicable for fulfilling steam need until 0,28 of mass fraction, which are represented by Pareto curve. CSP can fulfill all demand of steam funancially when the 10th unit established by learning curve projection. Fraction split of EFB into hydrolisis reactor is optimum at 0,25."