{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:75TXR2DDHYSCQKIGB24QMKQCEZ","short_pith_number":"pith:75TXR2DD","schema_version":"1.0","canonical_sha256":"ff6778e8633e242829060eb9062a0226579a31bb1b6703fd7c567675a7e860f7","source":{"kind":"arxiv","id":"2606.08611","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Liqiu Dong, Marta Zag\\'orowska, Mehmet Mercang\\\"oz","submitted_at":"2026-06-07T12:49:57Z","abstract_excerpt":"We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw-material, product, and utility prices. This preserves the structure of the economic objective, mak"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.08611","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.SY","submitted_at":"2026-06-07T12:49:57Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"f62b89bd29c33f3fee4556a69eaf86bad6f900570c65faa4cd382302e1eefbbc","abstract_canon_sha256":"15fa435cc5c310b24dbb3d3a6eec8cd3a610e40e6e9cbdd7a1e107e4eeaa8a18"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:41.428956Z","signature_b64":"+sCrB2mvSWyByDB7DII2O0kG4B5o9nJPOqJ/NrHRZxRMc6jZKKXDrtt6a8rd1p85TU0KFXZyJ2L0aOIR38mJCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff6778e8633e242829060eb9062a0226579a31bb1b6703fd7c567675a7e860f7","last_reissued_at":"2026-06-09T01:05:41.428537Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:41.428537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Optimization of a Multi-Product Chemical Reactor Using Composite Models and Partial Physics Knowledge","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Liqiu Dong, Marta Zag\\'orowska, Mehmet Mercang\\\"oz","submitted_at":"2026-06-07T12:49:57Z","abstract_excerpt":"We study data-driven real-time economic optimization of a multi-product chemical reactor when no reliable first-principles model is available beyond a steady-state energy balance. Instead of learning the economic objective directly as a black-box function, we use a composite formulation in which Gaussian process (GP) models predict physically meaningful outputs, including product concentrations and reactor temperature, while profit is computed analytically from these predictions together with raw-material, product, and utility prices. This preserves the structure of the economic objective, mak"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08611","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.08611/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.08611","created_at":"2026-06-09T01:05:41.428602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.08611v1","created_at":"2026-06-09T01:05:41.428602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08611","created_at":"2026-06-09T01:05:41.428602+00:00"},{"alias_kind":"pith_short_12","alias_value":"75TXR2DDHYSC","created_at":"2026-06-09T01:05:41.428602+00:00"},{"alias_kind":"pith_short_16","alias_value":"75TXR2DDHYSCQKIG","created_at":"2026-06-09T01:05:41.428602+00:00"},{"alias_kind":"pith_short_8","alias_value":"75TXR2DD","created_at":"2026-06-09T01:05:41.428602+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ","json":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ.json","graph_json":"https://pith.science/api/pith-number/75TXR2DDHYSCQKIGB24QMKQCEZ/graph.json","events_json":"https://pith.science/api/pith-number/75TXR2DDHYSCQKIGB24QMKQCEZ/events.json","paper":"https://pith.science/paper/75TXR2DD"},"agent_actions":{"view_html":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ","download_json":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ.json","view_paper":"https://pith.science/paper/75TXR2DD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.08611&json=true","fetch_graph":"https://pith.science/api/pith-number/75TXR2DDHYSCQKIGB24QMKQCEZ/graph.json","fetch_events":"https://pith.science/api/pith-number/75TXR2DDHYSCQKIGB24QMKQCEZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ/action/storage_attestation","attest_author":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ/action/author_attestation","sign_citation":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ/action/citation_signature","submit_replication":"https://pith.science/pith/75TXR2DDHYSCQKIGB24QMKQCEZ/action/replication_record"}},"created_at":"2026-06-09T01:05:41.428602+00:00","updated_at":"2026-06-09T01:05:41.428602+00:00"}