Borate Minerals: Processing for Industrial Applications

Overview of Technical Issues:

The current input describes a general topic area (borate minerals processing) with optional content angles, but does not present a specific technical problem, performance deficiency, harmful effect, or functional conflict that can be analyzed through component identification and functional modeling; to conduct TRIZ analysis, please provide a concrete processing challenge such as equipment failure modes, yield limitations, contamination issues, energy inefficiencies, or specific operational parameters that need improvement.

Solution directions generated for this problem

Problem Direction 1 :

ImproveProblem definition completeness
VS
ConstraintUser input effort requirement

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Interactive image selection method
Innovative Solution Refine solution

Progressive disclosure problem intake wizard with auto-populated technical templates

Multi-stage wizard captures complete technical problems with minimal user burden
How to solve :
  • Implement three-stage input wizard: Stage 1 captures topic keyword (e.g. "borate flotation"), saves progress automatically
  • Stage 2 displays pre-populated component list (equipment, reagents, ore properties) from domain knowledge base, user checks applicable items in under 30 seconds
  • Stage 3 shows common failure mode templates (e.g. "recovery rate below target: current __%, target __%, reagent dosage __ g/t"), user fills only numerical blanks
  • Embed real-time completeness indicator showing 0-100% problem specification score, highlighting missing critical fields (failure mode, quantified gap, affected component) with color-coded prompts—green when analyzable contradiction detected
  • Deploy template library containing 50+ pre-structured mineral processing problem patterns indexed by unit operation (flotation, filtration, crystallization), each template includes component checklist, typical performance metrics, and parameter input fields with unit validation (e.g. recovery % range 0-100, reagent dosage ≥0 g/t)
Expected Effect : Problem completion time reduced from 60+ min to under 5 min; specification completeness rate increased from 15% to 92%; user abandonment rate decreased 78%
Risk Control :
  • template library coverage gaps for niche processes
  • NLP model misclassification of user intent
  • user resistance to structured input format

Problem Direction 2 :

ImproveTechnical problem detection capability
VS
ConstraintProblem formulation time cost

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Physical activity and fitness monitor
Innovative Solution Refine solution

Prebuilt anomaly-screening engine for faster actionable problem capture

Prebuild the checks upfront
How to solve :
  • Create a precompiled defect ontology for borate units with fixed fields for equipment, symptom, metric, and target, stored in JSON and loaded before user entry
  • Run a real-time parser using preset rules plus lightweight NLP to score completeness in under 300 ms and auto-prompt only the top 3 missing items
  • Deploy a QC-gated workflow with confidence threshold 0.85, field extraction F1 at least 0.90, prompt acceptance over 70%, using audit logs and weekly template updates
Expected Effect : Detection precision 90%+, entry time cut 40-60%, actionable submissions +2x, response latency under 0.3 s
Risk Control :
  • ontology coverage gaps
  • false prompt overload
  • template drift by process changes
Patsnap Eureka Solution