Seyakakosha nzira isingawanzo simbi, tellurium inowana zvakakosha mashandisirwo mumasero ezuva, thermoelectric zvinhu, uye infrared kutariswa. Maitiro ekuchenesa echinyakare anotarisana nematambudziko akadai sekushomeka kwesimba, kushandiswa kwesimba kwakanyanya, uye kuchena kushoma kuvandudzwa. Ichi chinyorwa chinozivisa zvakarongeka kuti matekinoroji ehungwaru anogona sei kunyatso kwenenzvera nzira dzekuchenesa telurium.

1. Ikozvino Mamiriro eTellurium Purification Technology

1.1 Yakajairika Tellurium Kuchenesa Nzira uye Zvisingakwanisi

Nzira Dzakakura dzekuchenesa:

• Vacuum distillation: Inokodzera kubvisa yakaderera-inovira-poindi tsvina (semuenzaniso, Se, S)
• Kunatsa nzvimbo: Kunyanya kushanda pakubvisa tsvina yesimbi (semuenzaniso, Cu, Fe)
• Electrolytic refining: Inokwanisa kubviswa kwakadzika kwekusvibiswa kwakasiyana
• Chemical vapor transport: Inogona kugadzira yekupedzisira-yakakwirira-kuchena tellurium (6N giredhi uye pamusoro)

Matambudziko Akakosha:

• Maitiro maparamita anovimba neruzivo pane kurongeka optimization
• Kugona kubvisa tsvina kunosvika kumabhodhoro (kunyanya kutsvina isiri-simbi yakaita seokisijeni nekabhoni)
• Kushandiswa kwesimba kwakanyanya kunotungamira kumitengo yakakwira yekugadzira
• Yakakosha batch-to-batch kuchena kusiyana uye kusagadzikana kwakashata

1.2 Yakakosha Parameters yeTellurium Purification Optimization

Core Process Parameter Matrix:

2. AI Application Framework yeTellurium Purification

2.1 Yakazara Tekinoroji Architecture

Matatu-tier AI Optimization System:

1. Prediction layer: Machine kudzidza-based process mhedzisiro yekufembera modhi
2. Optimization layer: Multi-chinangwa parameter optimization algorithms
3. Kudzora layer: Real-time process control system

2.2 Kutora Data uye Kugadziridza System

Multi-source Data Integration Solution:

• Equipment sensor data: 200+ paramita zvinosanganisira tembiricha, kudzvanywa, kuyerera
• Maitiro ekutarisa data: Online misa spectrometry uye spectroscopic yekuongorora mhinduro
• Data yekuongorora marabhoritari: Offline yekuyedza mhinduro kubva kuICP-MS, GDMS, nezvimwe.
• Nhoroondo yekugadzira data: Marekodhi ekugadzira kubva kumakore mashanu apfuura (1000+ batches)

Feature Injiniya:

• Nguva-yakatevedzana inoratidza kudhirowa uchishandisa inotsvedza hwindo nzira
• Kuvakwa kwekusachena kutama kinetic features
• Kuvandudzwa kwe process parameter yekudyidzana matrices
• Kugadzwa kwezvinhu uye simba rekuenzanisa maficha

3. Yakadzama Core AI Optimization Technologies

3.1 Kudzidza Kwakadzika-Kwakavakirwa Maitiro Parameter Optimization

Neural Network Architecture:

• Input layer: 56-dimensional process parameters (yakajairika)
• Zvikamu zvakavanzwa: 3 LSTM zvidimbu (256 neurons) + 2 zvidimbu zvakazara
• Output layer: 12-dimensional mhando zviratidzo (kuchena, kusvibiswa kwemukati, nezvimwewo)

Maitiro ekudzidzisa:

• Kutamisa kudzidza: Pre-kudzidziswa uchishandisa yekuchenesa data yesimbi dzakafanana (semuenzaniso, Se)
• Kudzidza kwakasimba: Kunatsiridza madhizaini ekuedza kuburikidza neD-optimal methodology
• Kusimbisa kudzidza: Kumisikidza mibairo mabasa (kuchena kunatsiridza, kuderedza simba)

Typical Optimization Cases:

• Vacuum distillation tembiricha yeprofile optimization: 42% kuderedzwa muSe residue
• Zone refining rate optimization: 35% kunatsiridza mukubvisa Cu
• Electrolyte formulation optimization: 28% kuwedzera mukubudirira kwazvino

3.2 Zvidzidzo zveComputer-Aided Impurity Removal Mechanism

Molecular Dynamics Simulations:

• Kuvandudzwa kweTe-X (X=O,S,Se, nezvimwewo) kupindirana kunogona kuita mabasa
• Simulation yekusachena yekuparadzanisa kinetics pane tembiricha dzakasiyana
• Kufanotaura kwekuwedzera-kusachena kunosunga masimba

Kutanga-Simboti Maverengero:

• Kuverengera kwekusvibiswa kwekugadzira masimba mu tellurium lattice
• Kufanotaura kweakanakisa chelating mamorekuru zvimiro
• Optimization of vapor transport reaction pathways

Mienzaniso Yekushandisa:

• Kuwanikwa kwenovel oxygen scavenger LaTe₂, kuderedza okisijeni yemukati kusvika 0.3ppm
• Dhizaini yeakagadziridzwa chelating agents, kuvandudza kabhoni kubvisa kunyatsoshanda ne60%

3.3 Digital Twin uye Virtual Process Optimization

Digital Twin System Kuvaka:

1. Geometric modhi: Kwazvo 3D kuberekazve kwemidziyo
2. Yemuviri modhi: yakabatana kupisa kupisa, kutamisa kuwanda, uye fluid simba
3. Chemical modhi: Yakabatanidzwa kusachena reaction kinetics
4. Kudzora modhi: Simulated control system mhinduro

Virtual Optimization process:

• Kuedza 500+ maitiro musanganiswa munzvimbo yedhijitari
• Kuzivikanwa kweakakosha maparamita (CSV kuongororwa)
• Kufanotaura kweakanyanya kushanda windows (OWC kuongororwa)
• Probustness kusimbiswa (Monte Carlo simulation)

4. Industrial Implementation Pathway uye Benefit Analysis

4.1 Phased Implementation Plan

Chikamu I (0-6 mwedzi):

• Kuendeswa kweiyo basic data acquisition system
• Kugadzwa kwe process database
• Kuvandudzwa kwemaitiro ekutanga ekufanotaura
• Kuitwa kwekiyi parameter yekutarisa

Chikamu II (6-12 mwedzi):

• Kupedzwa kwedigital twin system
• Optimization ye core process modules
• Pilot yakavharwa-loop control kuita
• Quality traceability system development

Chikamu chechitatu (mwedzi 12-18):

• Yakazara-maitiro AI optimization
• Adaptive control systems
• Intelligent kuchengetedza masisitimu
• Kuenderera mberi kwekudzidza nzira

4.2 Mabhenefiti Anotarisirwa Ehupfumi

Nyaya Yekudzidza ye50-ton Year High-Purity Tellurium Production:

5. Matambudziko Unyanzvi uye Solutions

5.1 Makiyi Tekinoroji Mabhodhoro

1. Data Quality Issues:
   • Indasitiri data ine ruzha rwakakura uye hunhu husipo
   • Zviyero zvisingaenderane pazvinobva data
   • Marefu ekutora kutenderera kwepamusoro-kuchena kwekuongorora data
2. Muenzaniso Generalization:
   • Raw material kusiyana kunoita kuti modhi ikundikane
   • Kuchembera kwemidziyo kunokanganisa kugadzikana kwemaitiro
   • Zvitsva zvechigadzirwa zvinoda kudzidziswazve modhi
3. Matambudziko eSisitimu Yekubatanidza:
   • Kuenderana nyaya pakati yekare uye itsva michina
   • Kunonoka kwemhinduro yenguva chaiyo
   • Chengetedzo uye kuvimbika kwekuongorora matambudziko

5.2 Innovative Solutions

Adaptive Data Enhancement:

• GAN-based process data kugadzirwa
• Kutamisa kudzidza kutsiva kushomeka kwedata
• Semi-inotariswa kudzidza uchishandisa isina kunyorwa data

Hybrid Modelling Nzira:

• Fizikisi-yakamanikidzwa data mhando
• Mechanism-inotungamirwa neural network architectures
• Multi-fidelity modhi musanganiswa

Edge-Cloud Collaborative Computing:

• Edge deployment yeakakosha control algorithms
• Cloud computing kune yakaoma optimization mabasa
• Low-latency 5G kutaurirana

6. Remangwana Rekuvandudza Madirections

1. Intelligent Material Development:
   • AI-yakagadzirwa nyanzvi yekuchenesa zvinhu
   • High-throughput screening ye optimal additive musanganiswa
   • Kufanotaura kwenovel tsvina yekutora michina
2. Fully Autonomous Optimization:
   • Kuzvizivisa maitiro anoti
   • Self-optimizing maitiro ekushanda
   • Kuzvigadzirisa pachako kusarudzika
3. Green Purification Maitiro:
   • Minimum simba nzira optimization
   • Waste recycling mhinduro
   • Chaiyo-nguva kabhoni tsoka yekutarisa

Kuburikidza nekubatana kwakadzika kweAI, kucheneswa kwetellurium kuri kuita shanduko yeshanduko kubva kune chiitiko-inotungamirwa kuenda kune data-inotyairwa, kubva kune yakakamurwa optimization kuenda kune yakazara optimization. Makambani anorayirwa kuti atore "inyanzvi kuronga, kuita zvishoma nezvishoma" zano, kuisa pamberi pebudiriro mumatanho akakosha uye zvishoma nezvishoma kuvaka akazara hungwaru ekuchenesa masisitimu.